Shared farm intelligent management method and system, electronic device and storage medium

Abstract

This disclosure provides a method, system, electronic device, and storage medium for intelligent management of shared farms. The method includes: in response to a user terminal selecting a target plot, acquiring environmental background data associated with the target plot; generating and outputting planting suggestion information for the target plot based at least on the environmental background data; after planting begins, acquiring crop growth data collected by a monitoring device deployed on the target plot; dynamically adjusting prediction information associated with the planting suggestion information based on the crop growth data; and outputting the adjusted prediction information. This disclosure provides users with a solution for a fully intelligent planting management experience.

Description

Technical Field

[0001] This disclosure relates to the fields of smart agriculture and the sharing economy, and in particular to a smart management method, system, electronic device and storage medium for shared farms. Background Technology

[0002] With the development of the sharing economy, the "shared farm" model has emerged, allowing users to rent land and participate in planting remotely through online platforms. However, existing technological solutions generally suffer from the following bottlenecks: First, non-professional users find it difficult to scientifically select seeds based on specific plots' soil quality, climate, and other environmental data, leading to high planting risks; second, relying solely on static images or fixed-view videos cannot provide an intuitive and dynamic perception of crop growth, resulting in a weak user experience and sense of participation; third, the planting process lacks precise agricultural guidance based on real-time data and crop growth models, leading to extensive management; and fourth, data, services, and visualization elements are fragmented, failing to form an integrated intelligent management loop. These shortcomings restrict user experience and farm operational efficiency, resulting in user churn and low land reuse rates. Therefore, there is an urgent need for a solution that integrates environmental data, real-time monitoring, and visualization models to provide users with a fully intelligent planting management experience. Summary of the Invention

[0003] In view of this, the purpose of this disclosure is to provide a shared farm intelligent management method, system, electronic device and storage medium, providing users with a solution for a fully intelligent planting management experience.

[0004] In a first aspect, embodiments of this disclosure provide a smart management method for shared farms, the method comprising: in response to a user terminal selecting a target plot, acquiring environmental background data associated with the target plot; generating and outputting planting suggestion information for the target plot based at least on the environmental background data; after planting is initiated, acquiring crop growth data collected by a monitoring device deployed on the target plot; dynamically adjusting prediction information associated with the planting suggestion information based on the crop growth data; and outputting the adjusted prediction information.

[0005] Optionally, the environmental background data includes at least two of the following: historical soil data, historical climate data, agricultural expert knowledge base data, and local farmers' planting experience data; the crop growth data includes at least two of the following: crop visual data obtained through an image recognition device, temperature and humidity data obtained through an environmental sensor, and nutrient and moisture data obtained through a soil sensor.

[0006] Optionally, the prediction information includes at least one of the following: predicted growth cycle path, crop growth model, and agricultural operation prompt information; the dynamic adjustment of the prediction information based on the crop growth data includes: identifying the current growth stage and / or health status of the crop based on the crop growth data, and obtaining the identification result; and updating the predicted growth cycle path and / or the agricultural operation prompt information by combining the identification result with real-time environmental data.

[0007] Optionally, the agricultural operation prompts include: timing and dosage recommendations for at least one of fertilization, watering, weeding, pruning, and pest and disease control; the method further includes: sending the agricultural operation prompts to the farmer terminal serving the target plot.

[0008] Optionally, the adjusted prediction information output includes: generating and displaying a time-series-based three-dimensional visual dynamic growth model corresponding to the current growth stage.

[0009] Secondly, this disclosure provides a shared farm monitoring and management system for implementing the aforementioned intelligent management method for shared farms. The shared farm monitoring and management system includes: a user terminal and a data analysis and processing module; the user terminal is used to receive a user's selection instruction for a target plot and display information; the data analysis and processing module is communicatively connected to the user terminal and is used to: in response to the user terminal's selection instruction for the target plot, acquire environmental background data associated with the target plot; generate and output planting suggestion information for the target plot based at least on the environmental background data; receive crop growth data collected by a monitoring device deployed on the target plot; dynamically adjust prediction information associated with the planting suggestion information based on the crop growth data; and send the adjusted prediction information to the user terminal.

[0010] Optionally, the system further includes a real-time monitoring module deployed on the target plot, comprising at least two of an image recognition device, an environmental sensor, and a soil sensor, for collecting crop growth data and sending it to the data analysis and processing module.

[0011] Optionally, the system further includes: a third-party operation module, which is communicatively connected to the data analysis and processing module, for managing service provider information; wherein, the data analysis and processing module is specifically used to: after the user confirms the planting mode based on the planting suggestion information, match the service provider through the third-party operation module and generate an electronic agreement.

[0012] Thirdly, embodiments of this disclosure provide an electronic device, including: a processor and a memory; the processor and the memory are connected, wherein the memory is used to store a computer program, and the processor is used to invoke the computer program to execute the above-described intelligent management method for shared farms.

[0013] Fourthly, embodiments of this disclosure provide a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, perform the aforementioned shared farm intelligent management method.

[0014] The embodiments disclosed herein bring the following beneficial effects: The aforementioned intelligent management methods, systems, electronic devices, and storage media for shared farms provide scientific seed selection support for non-professional users through personalized planting suggestions based on plot environmental data, reducing planting risks and improving land utilization and planting success rates. By integrating real-time monitoring data to dynamically adjust and output growth predictions, they achieve intuitive and dynamic perception of crop growth status and precise and real-time planting management, greatly enhancing the user experience. They systematically solve the core pain points of existing shared farm models, such as unscientific decision-making, unintuitive perception, imprecise management, and lack of system coordination, providing effective technical means to optimize the allocation of agricultural resources, improve user satisfaction, promote the reclamation of abandoned land, and increase farmers' income.

[0015] Other features and advantages of this disclosure will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the disclosure. The objects and other advantages of this disclosure are realized and obtained through the structures particularly pointed out in the description, claims and drawings.

[0016] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the specific embodiments of this disclosure or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart of one embodiment of the intelligent management method for shared farms in this disclosure; Figure 2 This is a flowchart of another embodiment of the shared farm intelligent management method in this disclosure; Figure 3 This is a schematic diagram of a shared farm intelligent management system according to an embodiment of this disclosure. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0020] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in this disclosure, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” or “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] For ease of understanding, the specific process of the embodiments of this disclosure is described below. Please refer to [link / reference]. Figure 1 One embodiment of the shared farm intelligent management method in this disclosure includes: Step S101: In response to the user terminal's selection of the target plot, obtain the environmental background data associated with the target plot; This step corresponds to Figure 1 The "Plot Selection and Data Retrieval" section allows users to browse available plots on the shared farm platform and select one or more target plots through a terminal application (such as a mobile app). The user's terminal can be a smartphone, tablet, or personal computer.

[0022] Once a user selects a target plot, the system (specifically the data analysis and processing module) responds to this selection by retrieving uniquely associated environmental background data from a local or cloud database. This data is collected and stored in advance before the planting decision is made to assess the plot's suitability for planting.

[0023] Step S102: Based at least on environmental background data, generate and output planting suggestion information for the target plot; This step corresponds to Figure 1The "Planting Recommendation Generation" step involves a data analysis and processing module that runs a pre-defined algorithm model to comprehensively analyze the environmental background data obtained in step S101. The algorithm model can be constructed based on rule engines, machine learning models, or multi-criteria decision analysis methods.

[0024] The goal of the analysis is to assess which agricultural products are best suited for the current season and to predict their growth performance. The generated planting recommendations include at least one or more recommended agricultural product types. Typically, to assist user decision-making, this information may also include derivative information such as a summary of the predicted growth cycle, expected yield, management difficulty level, and estimated market value for each recommended crop.

[0025] The generated planting suggestions are output to the user through the user terminal's display interface (such as an APP page), and can be presented in the form of a list, card, or interactive chart.

[0026] Step S103: After planting begins, obtain crop growth data collected by monitoring devices deployed on the target plot; This step corresponds to Figure 1 The "real-time monitoring data collection" stage is crucial. Once the user confirms the planting plan (including selecting the crop and planting pattern) and completes the order, the planting activity officially begins. At this point, the real-time monitoring module deployed on the target plot starts working continuously.

[0027] The monitoring devices are a component of the real-time monitoring module. They are deployed in the field, either fixedly or mobilely, and include at least image recognition devices (such as cameras) for collecting visual data, sensors for collecting microclimate data (such as temperature and humidity sensors), and sensors for collecting soil data (such as soil moisture and nutrient sensors). These devices collect crop growth data at preset frequencies or through event-triggered methods and transmit the data to the data analysis and processing module via wired or wireless networks.

[0028] Step S104: Based on crop growth data, dynamically adjust the forecast information associated with planting recommendation information; This step corresponds to Figure 1 The "data fusion and dynamic adjustment" step is the core of this invention's precision management. The data analysis and processing module continuously receives crop growth data uploaded in step S103.

[0029] Predictive information refers to expectations about the future growth of crops that are implicit in or derived from the planting recommendations generated in step S102, such as the "growth cycle path" initially predicted based on historical data. In this step, the system dynamically adjusts this predictive information using real-time collected growth data.

[0030] The adjustment logic is to compare and integrate real-time monitoring data with the initial model. For example, if image data shows that crop growth is slower than expected, the system may combine current soil nutrient and temperature data to determine whether it is due to insufficient nutrients or insufficient accumulated temperature, thereby revising the predicted maturity date. This process is continuous and cyclical, ensuring that the prediction is always based on the latest conditions.

[0031] Step S105: Output the adjusted prediction information.

[0032] This step corresponds to Figure 1 The "Visualized Information Push" step involves dynamically adjusting the forecast information in step S104, then feeding it back to users and relevant parties.

[0033] The data analysis and processing module formats the adjusted information (e.g., updated estimated harvest time, new pest and disease risk warnings, adjusted optimal irrigation time windows, etc.) and pushes it to the user's terminal. Users can view these updates on the app, thus obtaining management intelligence synchronized with the actual situation in the field.

[0034] The shared farm intelligent management method provided by the above-described implementation method offers scientific seed selection support to non-professional users through personalized planting suggestions based on plot environmental data, reducing planting risks and improving land utilization and planting success rates. By integrating real-time monitoring data to dynamically adjust and output growth predictions, it achieves intuitive and dynamic perception of crop growth status and precise and real-time planting management, greatly enhancing the user experience. It systematically solves the core pain points of existing shared farm models, such as unscientific decision-making, unintuitive perception, imprecise management, and lack of system coordination. It provides an effective technical means to optimize the allocation of agricultural resources, improve user satisfaction, promote the reclamation of abandoned land, and increase farmers' income.

[0035] Next, we will explain the specific intelligent management methods for shared farms.

[0036] In one embodiment, the environmental background data includes at least two of the following: historical soil data, historical climate data, agricultural expert knowledge base data, and local farmers' planting experience data; the crop growth data includes at least two of the following: crop visual data obtained through an image recognition device, temperature and humidity data obtained through an environmental sensor, and nutrient and moisture data obtained through a soil sensor.

[0037] Historical soil data can be past soil testing reports for the plot, including pH value, organic matter content, and the content of macroelements such as nitrogen, phosphorus, and potassium; historical climate data can be meteorological statistics for the region over many years, including average temperature, precipitation, sunshine hours, and frost-free period; agricultural expert knowledge base data can be crop cultivation knowledge stored in a structured form, such as "what soil pH range is best for a certain crop" or "accumulated temperature requirements for a certain variety"; local farmers' planting experience data can be unstructured or semi-structured data obtained through surveys or data entry regarding the types of crops successfully grown on the plot or similar plots, crop rotation habits, fertilization experience, etc.

[0038] Crop growth data includes at least two of the following: visual crop data acquired through image recognition devices, temperature and humidity data acquired through environmental sensors, and nutrient and moisture data acquired through soil sensors. Specifically, the visual crop data acquired through image recognition devices can be periodically taken photographs of the crop canopy, used to analyze leaf color, plant height, canopy width, flowering and fruiting status, etc.; the temperature and humidity data acquired through environmental sensors can be real-time local environmental data recorded by temperature and humidity sensors deployed at the height of the crop canopy; and the nutrient and moisture data acquired through soil sensors can include data collected by soil moisture sensors and soil EC (electrical conductivity) sensors, used to reflect soil moisture and soluble salt concentration (indirectly reflecting nutrient concentration).

[0039] In one embodiment, the prediction information includes at least one of the following: a predicted growth cycle path, a crop growth model, and agricultural operation prompts; the prediction information is dynamically adjusted based on crop growth data, including: identifying the current growth stage and / or health status of the crop based on the crop growth data to obtain an identification result; and updating the predicted growth cycle path and / or agricultural operation prompts by combining the identification result with real-time environmental data.

[0040] The aforementioned predicted growth cycle path can be used to indicate: the various growth stages (such as germination, seedling stage, flowering, fruit setting, maturity, etc.) that a crop is expected to go through from planting to harvest, and their key time points, in the form of a timeline; the crop growth model can be a digital model that can visualize and simulate the crop morphology; and the agricultural operation prompts can include suggestions to guide users or farmers in field management.

[0041] When identifying the current growth stage and / or health status of crops, the current growth stage (such as tillering stage, jointing stage) and / or health status (such as whether leaf spots, wilting, or pest symptoms appear) of crops can be analyzed by using image recognition algorithms based on crop visual data in crop growth data. Specifically, by comparing with image feature databases, it can be determined that rice is in the "peak tillering stage" to determine the current growth stage and / or health status.

[0042] When updating the predicted growth cycle path and / or agricultural operation prompts, the prediction information can be updated by combining the above identification results with real-time environmental data (such as current soil moisture and temperature) obtained from the real-time monitoring module. For example, if the system identifies that the crop is in the "flowering stage" and the real-time environmental data shows continuous high temperature and drought, the system can update the predicted growth path, indicating that the flowering period may be shortened, and adjust the prompt for the "irrigation" agricultural operation, suggesting that the irrigation time be brought forward.

[0043] In one implementation, the agricultural operation prompts include: timing and dosage recommendations for at least one of fertilization, watering, weeding, pruning, and pest and disease control; the method further includes: sending the agricultural operation prompts to a farmer terminal serving the target plot.

[0044] The above-mentioned agricultural operation tips specifically include recommendations on the timing and dosage of at least one of the following field management practices: fertilization, watering, weeding, pruning, and pest and disease control. For example, "It is recommended to apply topdressing in 3 days, using 10 kg of urea per mu," or "Current humidity conditions are prone to induce downy mildew, so it is recommended to spray with a 500-fold dilution of 80% mancozeb wettable powder for prevention."

[0045] Sending agricultural operation prompts to the terminals of farmers serving the target plots is necessary because, in actual "contract farming" or "joint farming" models, the farmers directly performing field operations are the ones entrusted with the work. Therefore, the system synchronously pushes scientific management suggestions to the actual implementers, ensuring that the prompts are implemented. User terminals will also receive this information to monitor and understand the field management situation.

[0046] In one implementation, the output of adjusted prediction information includes generating and displaying a time-series-based, visually dynamic growth model corresponding to the current growth stage. This 3D visually dynamic growth model is a virtual crop model constructed using computer graphics technology. The data analysis and processing module, based on the currently identified growth stage (e.g., "flowering stage"), calls the corresponding 3D model template and fine-tunes the model parameters using real-time data (e.g., plant height, leaf area index) to make its shape closely resemble that of a real crop. Then, the model is rendered and displayed on the user's terminal screen. As time progresses and data continues to be input, the model dynamically changes, simulating the continuous process of crop growth and providing the user with an immersive observation experience. "Time-series-based" means that the model's morphological changes are driven by data derived from crop growth data collected sequentially over time.

[0047] Another embodiment of the shared farm intelligent management method in this disclosure includes the following steps: S1: The user selects a target rental plot through the terminal application (APP); S2: The data analysis and processing module responds to the user's selection, retrieving environmental background big data associated with the plot. This environmental background big data includes at least historical and real-time soil data, climate data, seasonal information, an agricultural expert knowledge base, and local farmers' planting experience data; S3: Based on the environmental background big data, the system analyzes the data using an algorithm model and outputs a personalized planting suggestion list for the plot in the current season to the user's terminal. The list includes recommended agricultural products and their predicted growth cycle paths; S4: Based on the suggestion list, the user selects the agricultural products to be planted and confirms the planting mode, which includes self-planting, contracted planting, or joint planting; the system then generates... Orders are placed and service providers are matched, facilitating the signing of electronic agreements between both parties; S5: After planting begins, the real-time monitoring module deployed on the plot starts working, continuously collecting multi-dimensional dynamic growth data; the multi-dimensional dynamic growth data includes at least crop visual data obtained by image recognition devices, temperature and humidity data obtained by environmental sensors, and nutrient and moisture data obtained by soil sensors; S6: The data analysis and processing module dynamically integrates environmental background big data with multi-dimensional dynamic growth data; S7: The updated predicted growth cycle path, three-dimensional visual dynamic growth model, and agricultural operation prompts are pushed to the user terminal for visualization; S8: After the crop matures, the user selects the harvesting method and logistics plan through the terminal, completing the service loop.

[0048] The predicted growth cycle path in step S3 is calibrated and dynamically adjusted in real time, and a three-dimensional visual dynamic growth model of the crop and agricultural operation prompts based on the current growth stage and environmental conditions are generated or updated simultaneously. The agricultural operation prompts include suggestions on the timing and dosage of fertilization, watering, weeding, pruning, bagging, and pest and disease control.

[0049] like Figure 2 As shown in the embodiment of this disclosure, the implementation process of the intelligent management method for shared farms is as follows: The user opens the shared farm APP (user terminal), browses and selects a specific plot of land located in a village in a certain province. Based on the region selected by the user, the system retrieves big data on the local environment, soil quality, season, and suggestions from agricultural experts and local farmers. After analysis, it displays a list of agricultural products currently suitable for planting, including information such as the growth cycle and yield forecast of various agricultural products. The user selects planting objects from the list according to their own needs and preferences, chooses a planting mode (self-planting, commissioned planting, joint planting), fills in relevant information such as planting area, and forms a planting order.

[0050] The system (data analysis and processing module) immediately retrieves historical data on soil pH, nitrogen, phosphorus and potassium content, local climate data for the same period in the past five years, and current seasonal information for the plot, and conducts a comprehensive analysis by combining this data with successful planting cases in the cloud-based agricultural expert knowledge base for the region.

[0051] The app interface (data display module) presents users with a suggested list: for the current season, your plot is best suited for planting either "Sunshine Rose" grapes (prediction period 120 days) or "Strawberries" (prediction period 90 days), along with brief descriptions of their growth stages. Users select "Strawberries" based on their interest and choose the "Contract Planting" mode. The system, through a third-party service interface module, pushes the order to Mr. Li, a farmer registered near the plot with a high rating. After both parties confirm the electronic agreement on the app, planting begins. Once Mr. Li has completed planting, the real-time monitoring module on the plot starts working: a high-definition camera (image recognition device) periodically captures images of the strawberry seedlings; a temperature and humidity sensor (environmental monitoring device) collects microenvironmental data; and a soil probe (soil nutrient monitoring device) monitors humidity and EC values. This data is uploaded in real time.

[0052] After receiving the first batch of image data, the data analysis and processing module activates the image recognition algorithm to determine that the strawberry seedlings are in the "seedling establishment period." Combined with the low soil moisture data, the system automatically adjusts the predicted growth path of this batch of strawberries and generates a corresponding initial seedling model in the 3D engine. Simultaneously, the system pushes the first agricultural operation prompt to farmer Mr. Li's APP and the user's APP: the strawberries are currently in the seedling establishment period; it is recommended to water them thoroughly tomorrow evening, with an irrigation volume of approximately XX liters per square meter. Subsequently, as the strawberries grow, the system continuously integrates new data. When image recognition combined with data from the pest monitoring device identifies a potential aphid risk, the system updates the model (e.g., displaying warning dots on the model leaves) and pushes prevention and control suggestions. Users can see a realistic 3D strawberry plant growing synchronously with their own field on their mobile phones, much like playing a simulation management game, and can view prompts for each key growth node.

[0053] Once the system is ready, users will receive a notification on the app and can choose between "express delivery" or "in-person pickup" in the shipping module to complete the entire experience.

[0054] In one embodiment, this disclosure also provides a shared farm monitoring and management system, the system comprising: a user terminal and a data analysis and processing module; wherein, the user terminal is used to receive a user's selection instruction for a target plot and display information; the data analysis and processing module is used to communicate with the user terminal and is used to: in response to the user terminal's selection instruction for the target plot, acquire environmental background data associated with the target plot; generate and output planting suggestion information for the target plot based at least on the environmental background data; receive crop growth data collected by monitoring devices deployed on the target plot; dynamically adjust the prediction information associated with the planting suggestion information based on the crop growth data; and send the adjusted prediction information to the user terminal.

[0055] The shared farm monitoring and management system provided by the above-described implementation method offers scientific seed selection support to non-professional users through personalized planting suggestions based on plot environmental data, reducing planting risks and improving land utilization and planting success rates. By integrating real-time monitoring data to dynamically adjust and output growth predictions, it achieves intuitive and dynamic perception of crop growth status and precise and real-time planting management, greatly enhancing the user experience. It systematically solves the core pain points of existing shared farm models, such as unscientific decision-making, unintuitive perception, imprecise management, and lack of system coordination. It provides an effective technical means to optimize the allocation of agricultural resources, improve user satisfaction, promote the reclamation of abandoned land, and increase farmers' income.

[0056] The aforementioned user terminal has input and display functions. Its input module is used to receive user instructions on selecting target plots, making planting decisions, and selecting planting patterns; its display module is used to visually present all information to the user.

[0057] The aforementioned data analysis and processing module is used for data storage, processing, and analysis. Specifically, it performs data processing, model calculations, and logical judgments to execute the aforementioned intelligent management method for shared farms.

[0058] In one embodiment, the system further includes a real-time monitoring module deployed on the target plot, comprising at least two of an image recognition device, an environmental sensor, and a soil sensor, for collecting crop growth data and sending it to the data analysis and processing module.

[0059] Image recognition devices, such as fixed cameras, orbital scanning cameras, or drones, are used to acquire visual data of crops; environmental sensors, such as air temperature and humidity sensors, light sensors, and rainfall sensors, are used to acquire microclimate data of crop canopies; and soil sensors, such as soil temperature and humidity sensors, soil pH sensors, and soil nitrogen, phosphorus, and potassium sensors, are used to acquire environmental data of the root zone.

[0060] In one implementation, the system further includes a third-party operation module, which is communicatively connected to the data analysis and processing module and is used to manage service provider information; wherein, the data analysis and processing module is specifically used to: after the user confirms the planting mode based on the planting suggestion information, match the service provider through the third-party operation module and generate an electronic agreement.

[0061] After the user confirms the planting mode (such as contract farming) based on the planting suggestions, the data analysis and processing module invokes the functions of the third-party operation module. Specifically, based on order requirements (such as plot location, crop type, and area), the data analysis and processing module uses the matching algorithm of the third-party operation module to select suitable farmers or organizations from the registered service providers. Subsequently, the system generates an electronic agreement through this module and facilitates online signing between the user and the service provider, thereby automating the service matching and contract signing process.

[0062] Figure 3 The diagram shown is a schematic representation of a shared farm monitoring and management system provided in this disclosure, including: a user terminal, a data analysis and processing module, a real-time monitoring module, and a third-party operation module. The user terminal, the third-party operation module, and the real-time monitoring module all establish communication connections (such as wired or wireless networks) with the data analysis and processing module. The data analysis and processing module is connected to the display module of the user terminal for pushing information to it.

[0063] In one embodiment, this disclosure also provides a shared farm monitoring and management system, the system comprising: The system comprises a user terminal, a data analysis and processing module, a third-party operation module, a real-time monitoring module, and a data output module. The user terminal includes an input module and a data display module. The input module receives user information regarding plot selection, planting decisions, planting patterns, and logistics instructions. The data display module provides users with visual information about planting suggestions, predicted growth paths, a 3D dynamic growth model, operational prompts, and logistics status. The data analysis and processing module, as the core of the system, stores, processes, and analyzes all data. Specifically, it generates personalized planting suggestions based on user-input plot information and accesses environmental background big data; it organizes order contract data based on the user's final decision; it receives and processes multi-dimensional dynamic growth data uploaded by the real-time monitoring module; and it runs algorithm models to dynamically generate and update predicted growth paths, the 3D dynamic growth model, and operational prompts. The third-party operation module accesses and manages service information and order agreements from farmers or planting organizations. The real-time monitoring module, deployed in the field, includes image recognition devices, environmental monitoring devices, soil nutrient monitoring devices, and pest monitoring devices, and feeds the monitoring data back to the data analysis and processing module. The input module, third-party operation module, and real-time monitoring module are all connected to the data analysis and processing module; the data analysis and processing module is connected to the data display module.

[0064] like Figure 3As shown, the shared farm monitoring and management system provided in this embodiment operates collaboratively through IoT and mobile internet technologies, forming an organic whole and realizing synchronous mapping and intelligent management of the physical farm and its digital twin. The shared farm monitoring and management system specifically includes: a user terminal, a data analysis and processing module, a third-party operation module, and a real-time monitoring module. The user terminal includes an input module and a data display module; the input module receives the user's plot selection, planting decisions, mode selection, and logistics instructions; the data display module visualizes planting suggestions, predicted growth paths, a three-dimensional visual dynamic growth model, agricultural operation prompts, and logistics status for the user. The data analysis and processing module, as the core of the system, stores, processes, and analyzes all data. Specifically, it uses the plot information input by the user to call up environmental background big data to generate personalized planting suggestions; it organizes order contract data based on the user's final decision; it receives and processes multi-dimensional dynamic growth data uploaded by the real-time monitoring module; and it runs algorithm models to dynamically generate and update predicted growth paths, three-dimensional visual dynamic growth models, and agricultural operation prompts. The third-party operation module accesses and manages the service information and order acceptance agreements of farmers or planting institutions. The real-time monitoring module, deployed in the field, includes image recognition devices, environmental monitoring devices, soil nutrient monitoring devices, and pest monitoring devices, and feeds the monitoring data back to the data analysis and processing module. The input module, third-party operation module, and real-time monitoring module are all connected to the data analysis and processing module; the data analysis and processing module is connected to the data display module.

[0065] In one embodiment, this disclosure also provides a shared farm intelligent management method and a shared farm monitoring and management system for implementing the above method. The user terminal selects a plot of land; in response to the plot selection, it retrieves environmental background big data associated with the plot; based on the environmental background big data, it generates and outputs a personalized planting suggestion list to the user terminal; it receives the user's decision based on the list, confirms the planting mode, and forms a service order and agreement; after planting begins, it collects multi-dimensional dynamic growth data of the crop growth site through a real-time monitoring module; it dynamically integrates environmental background big data and multi-dimensional dynamic growth data to generate and update the predicted growth cycle path, visual dynamic growth model, and agricultural operation prompts for the crop in real time; and it pushes the updated information to the user terminal for display. By integrating multi-dimensional data such as soil quality, climate, season, and expert knowledge, it provides users with accurate planting suggestions, helping them better select agricultural products suitable for local planting and improving planting success rates. Combining real-time monitoring data and agricultural experience and technical data, it forms a predicted growth path for crops and a real-time dynamic model of the field, enabling users to intuitively understand the crop growth situation and enhancing user participation and experience. Based on real-time crop growth status and specific environmental parameters, precise agricultural operation prompts are provided, enabling scientific and refined planting management, which helps improve the quality and yield of agricultural products. A fully intelligent closed loop has been constructed, from planting decisions and process management to harvesting and distribution, efficiently integrating elements such as land, technology, manpower, and data. This improves the overall operational efficiency and user experience of shared farms, promotes the reclamation of abandoned land, increases land utilization, increases farmers' income, and drives agricultural development.

[0066] This embodiment also provides an electronic device, including: a processor and a memory; the processor and the memory are connected, wherein the memory is used to store a computer program, and the processor is used to call the computer program to execute the above-described intelligent management method for shared farms.

[0067] This embodiment also provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, the computer program including program instructions, and the program instructions, when executed by a processor, execute the above-described intelligent management method for shared farms.

[0068] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0069] Furthermore, in the description of the embodiments of this disclosure, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this disclosure based on the specific circumstances.

[0070] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0071] In the description of this disclosure, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0072] Finally, it should be noted that the above embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A smart management method for shared farms, characterized in that, Including the following steps: In response to the user terminal's selection of a target plot, obtain the environmental background data associated with the target plot; Based at least on the aforementioned environmental background data, planting recommendation information for the target plot is generated and output; After planting begins, crop growth data is acquired through monitoring devices deployed on the target plot. Based on the crop growth data, the prediction information associated with the planting recommendation information is dynamically adjusted; Output the adjusted prediction information.

2. The method according to claim 1, characterized in that, The environmental background data includes at least two of the following: historical soil data, historical climate data, agricultural expert knowledge base data, and local farmers' planting experience data. The crop growth data includes at least two of the following: crop visual data acquired through an image recognition device, temperature and humidity data acquired through an environmental sensor, and nutrient and moisture data acquired through a soil sensor.

3. The method according to claim 1, characterized in that, The prediction information includes at least one of the following: predicted growth cycle path, crop growth model, and agricultural operation prompts; the dynamic adjustment of the prediction information based on the crop growth data includes: The current growth stage and / or health status of the crop are identified based on the crop growth data, and an identification result is obtained; Based on the identification results and real-time environmental data, update the predicted growth cycle path and / or the agricultural operation prompts.

4. The method according to claim 3, characterized in that, The agricultural operation prompts include: timing and dosage recommendations for at least one of the following: fertilization, watering, weeding, pruning, and pest and disease control; the methods also include: The agricultural operation prompts are sent to the farmer terminals serving the target plot.

5. The method according to claim 2, characterized in that, The adjusted prediction information includes: generating and displaying a time-series-based three-dimensional visual dynamic growth model corresponding to the current growth stage.

6. A shared farm monitoring and management system, characterized in that, For implementing the method according to any one of claims 1-5, the shared farm monitoring and management system includes: a user terminal and a data analysis and processing module; The user terminal is used to receive the user's selection instruction for the target plot and display the information; The data analysis and processing module, used for communication connection with the user terminal, is used for: In response to the user terminal's selection instruction for the target plot, environmental background data associated with the target plot is obtained; Based at least on the aforementioned environmental background data, planting recommendation information for the target plot is generated and output; Receive crop growth data collected by monitoring devices deployed on the target site; Based on the crop growth data, the prediction information associated with the planting recommendation information is dynamically adjusted; The adjusted prediction information is sent to the user terminal.

7. The system according to claim 6, characterized in that, The system also includes: A real-time monitoring module, deployed on the target plot, includes at least two of the following: an image recognition device, an environmental sensor, and a soil sensor, used to collect crop growth data and send it to the data analysis and processing module.

8. The system according to claim 6, characterized in that, The system also includes: A third-party operation module, which communicates with the data analysis and processing module, is used to manage service provider information; Specifically, the data analysis and processing module is used to: after the user confirms the planting mode based on the planting suggestion information, match the service provider through the third-party operation module and generate an electronic agreement.

9. An electronic device, characterized in that, include: Processor and memory; The processor is connected to a memory, wherein the memory is used to store a computer program, and the processor is used to invoke the computer program to perform the method as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, perform the method as described in any one of claims 1-5.

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