A student-oriented campus green plant irrigation management interaction method and system

Abstract

The application provides a student-oriented campus green plant irrigation management interaction method and system, and relates to the technical field of smart campus. The method divides the campus greening area into multiple target irrigation areas and binds student users, so that each student can submit irrigation instructions through the irrigation interaction interface on the mobile terminal. Combined with the historical meteorological data of the area, the cumulative water consumption is accurately calculated, and the real-time growth curve reflecting the current growth status of the plants is generated according to the mapping relationship between the plant species, water consumption and growth status. Further, the growth deviation is calculated by further combining the preset reference planting curve and the correction factor determined based on the user behavior data, and finally the visual form of the corresponding three-dimensional virtual green plant model on the mobile terminal is dynamically adjusted, so that the closed-loop linkage of environmental perception, user operation and plant growth status is realized at the technical level.

Description

Technical Field

[0001] This application relates to the field of smart campus technology, and in particular to a management and interaction method and system for watering campus green plants for students. Background Technology

[0002] Currently, campus green plant maintenance largely relies on manual inspections or simple automated irrigation equipment triggered by fixed thresholds, lacking the ability to perceive and respond to student user participation. While existing intelligent irrigation systems can control the start and stop of water pumps based on soil moisture sensors or weather forecast data, their control logic typically uses static threshold judgments and fails to dynamically adjust for the actual water requirements of plants based on real-time rainfall, leading to over-irrigation or insufficient water supply. Furthermore, the system's interactive interfaces are mostly status indicator lights or backend management interfaces, unable to provide non-professional users such as students with understandable feedback information related to their actions, such as whether this irrigation is necessary, whether the cumulative water usage is redundant, and how environmental factors affect the plant's water needs. Summary of the Invention

[0003] This application provides a management and interactive method and system for watering campus green plants for students, in order to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions that can dynamically adjust the watering strategy based on real-time environmental data and user interaction behavior. Through a visual feedback mechanism, the system integrates and calculates the water demand status of plants, historical watering records and actual rainfall to generate personalized task prompts and effect evaluations. Thus, while ensuring the healthy growth of green plants, it constructs a quantifiable, traceable, and closed-loop feedback student-participatory intelligent watering system.

[0004] On the one hand, this application provides a student-oriented management and interaction method for watering campus green plants, including: The green areas on campus are divided into multiple target irrigation zones, and each target irrigation zone is associated with one or more students. A watering interactive interface is provided to students through a mobile terminal device with an installed watering interactive application. The watering interactive interface includes watering control operation controls to receive watering instructions input by students for their bound target watering area. The watering instructions include at least the water volume, watering frequency, and watering time. Obtain historical meteorological data corresponding to the target irrigation area; Based on the irrigation instructions input by the students and the rainfall information in the historical meteorological data, the cumulative water consumption of the target irrigation area is calculated. Based on a preset mapping table of plant species, water consumption, and growth status, a real-time growth curve representing the current growth status of the plant is generated according to the cumulative water consumption. The growth deviation is calculated based on the real-time growth curve, the preset reference planting curve, and the correction factor determined based on user behavior data. Based on the growth deviation, the visualization of the three-dimensional virtual green plant model displayed on the mobile terminal device, corresponding to the target irrigation area, is dynamically adjusted.

[0005] Furthermore, the step of dividing the green areas on campus into multiple target irrigation zones and establishing a binding relationship between each target irrigation zone and one or more students includes: Pre-configure multiple target irrigation areas and their corresponding plant species information; In response to a student's adoption request initiated through the mobile terminal device, a list of target irrigation areas that can be adopted is pushed to the student; The system receives the target irrigation area selected by the student from the list and assigns the target irrigation area to a student group consisting of one or more students. At the same time, the system synchronizes the plant species, ideal growth cycle and reference planting curve of the target irrigation area with the student group members.

[0006] Furthermore, the irrigation instruction also includes a specific irrigation time; The correction factor determined based on user behavior data is generated according to the following steps: After the students set the watering time, the forecast meteorological data of the target watering area for the next 24 hours is obtained. The forecast meteorological data includes rainfall, daily maximum temperature, daily minimum temperature and relative humidity. Based on the forecast meteorological data, the effective water requirement correction coefficient for plants on that day is calculated, and a recommended irrigation amount is generated accordingly, which is displayed on the mobile terminal device for students' reference. Record the deviation ratio between the water volume finally confirmed by the student and the recommended water volume, and use this deviation ratio as a correction factor determined based on user behavior data for subsequent calculation of growth deviation.

[0007] Furthermore, the effective water demand correction coefficient Calculate using the following formula: ; in, Forecast rainfall for the next 24 hours, The maximum daily evapotranspiration requirement of the plants planted in the target irrigation area at the current growth stage is obtained by referring to the table based on the planting curve; The recommended irrigation water volume Generate using the following formula: ; in, This is the standard daily watering amount for the plant at its current growth stage.

[0008] Furthermore, the historical meteorological data includes the actual daily rainfall over the past 7 days; The step of calculating the cumulative water consumption of the target irrigation area based on the irrigation instructions input by the students and the rainfall information in the historical meteorological data includes: The effective historical rainfall is obtained by weighting and summing the actual daily rainfall over the past 7 days, with the weights arranged in reverse chronological order using an exponential decay function. Perform calculations, where It represents the number of days from the current date. The soil moisture dissipation coefficient ranges from 0.1 to 0.5. The cumulative water consumption is obtained by adding the irrigation water volume entered by the student to the effective historical rainfall. If the actual daily rainfall exceeds the maximum daily water absorption threshold of the roots of the plants planted in the target irrigation area, the excess portion will not be included in the effective historical rainfall.

[0009] Further, the calculation of the growth deviation based on the real-time growth curve, the preset reference planting curve, and the correction factor determined based on user behavior data includes the following steps: Under the same time coordinate, the integral area of ​​the real-time growth curve and the preset reference planting curve within the preset observation period is calculated, and the ratio of the integral area of ​​the real-time growth curve to the integral area of ​​the reference planting curve is used as the basic growth deviation. The baseline growth deviation is weighted and adjusted using the correction factor to obtain the final growth deviation.

[0010] Furthermore, the mobile terminal device is equipped with a plant growth morphology display area for rendering the three-dimensional virtual green plant model; The step of dynamically adjusting the visualization of the three-dimensional virtual green plant model displayed on the mobile terminal device, corresponding to the target irrigation area, based on the growth deviation includes: A multi-level virtual morphology library is pre-built for each plant type, with each level corresponding to a preset growth deviation range; Based on the currently calculated growth deviation, the corresponding morphological level is matched, and a three-dimensional virtual green plant model of the corresponding level is rendered in the display area. When the growth deviation exceeds the preset warning value for three consecutive days, a withering animation effect is superimposed on the virtual model, and a maintenance reminder message is pushed to the mobile terminal device.

[0011] Furthermore, the method also includes: in response to a real-scene photo of the plants in the target irrigation area uploaded by a student through the mobile terminal device, comparing and displaying the real-scene photo with the plant growth status predicted based on the cumulative water consumption; The comparison presentation includes: The leaf area index or plant height of the real-world photos is estimated using a pre-trained convolutional neural network model. The estimated results were compared with the theoretical growth indicators predicted based on the current cumulative water consumption and the reference planting curve. The real-world photo and the three-dimensional virtual plant model are displayed side-by-side on the mobile terminal device, and the quantitative differences between the two in terms of plant height, crown width, or leaf density are marked.

[0012] Furthermore, the mobile terminal device also provides a communication community module, which allows students who are bound to different target irrigation areas to upload irrigation experience texts and plant growth records in the communication community; The communication community module is also equipped with a knowledge Q&A sub-module, which is associated with a pre-built plant care knowledge graph. When a student asks a question about plant growth in the communication community, the system automatically retrieves matching cause analysis and treatment suggestions from the knowledge graph and pushes them to the mobile terminal devices of the student who asked the question and the student group in the form of structured cards.

[0013] On the other hand, this application provides a management and interactive system for watering campus green plants for students, including a central control terminal deployed on a campus server or cloud platform and multiple mobile terminal devices with watering interactive applications installed. The central control terminal is configured to manage multiple target irrigation areas within the campus and execute the aforementioned student-oriented campus green plant irrigation management interaction method. The mobile terminal device is configured to provide an interactive watering interface, display a 3D virtual green plant model, upload real-world photos, and participate in community interactions.

[0014] The beneficial effects of this application are as follows: This application provides a management and interactive method and system for watering campus green plants for students. This method divides the campus green area into multiple target watering zones and binds them to student users. Each student can submit instructions including water volume and frequency through a watering interface on a mobile terminal. The system combines historical meteorological data, especially rainfall information, for the area to accurately calculate the cumulative water consumption. Based on the mapping relationship between plant species, water consumption, and growth status, it generates a real-time growth curve reflecting the current growth status of the plants. Furthermore, it integrates a preset reference planting curve and a correction factor determined based on user behavior data to calculate the growth deviation. Finally, it dynamically adjusts the visualization of the corresponding three-dimensional virtual green plant model on the mobile terminal, thus achieving a closed-loop linkage between environmental perception, user operation, and plant growth status at the technical level. This allows students to intuitively and quantitatively understand the actual impact of their watering behavior on plant growth and effectively avoids resource waste caused by repeated or excessive watering. This application also provides a corresponding system, the beneficial effects of which are similar to the method and will not be elaborated here.

[0015] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0016] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0017] Figure 1 This is a flowchart of the student-oriented campus green plant watering management interaction method provided in this application; Figure 2 This is a structural diagram of the student-oriented campus green plant watering management and interaction system provided in this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] The present application will be further described below with reference to the accompanying drawings and specific embodiments. The described embodiments should not be considered as limitations on the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0020] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] In the context of current smart and green campus construction, campus green plant maintenance is gradually evolving from traditional manual methods to intelligent systems. However, existing technological systems still have significant shortcomings in participatory management for students. Currently, mainstream green plant maintenance methods fall into two categories: one relies on regular inspections and manual watering by support staff, depending entirely on experience to determine whether plants need irrigation; the other uses basic automated irrigation systems, typically based on soil moisture sensors or timers to control the start and stop of water pumps. While these systems reduce manpower to some extent, their design focuses on horticultural maintenance efficiency, neglecting the cognitive level, operating habits, and motivations of non-professional users, especially students, in educational settings.

[0023] In recent years, some smart gardening equipment has incorporated Internet of Things (IoT) technology. For example, it uses wireless sensor networks to collect data on soil temperature and humidity, light intensity, and weather forecasts, and then automatically triggers irrigation based on preset thresholds. Some advanced systems also have remote control capabilities, allowing users to start or stop watering via a mobile app. However, these systems generally suffer from rigid interactive logic and a lack of feedback mechanisms. Their user interfaces are often just status displays or simple on / off controls, failing to clearly convey crucial information to students such as "why watering is needed," "is this watering necessary," and "how environmental factors affect plant water requirements." More importantly, existing systems generally do not perform refined processing of natural rainfall, often using only whether it has rained as a binary criterion or directly ignoring the actual replenishment of soil moisture by historical rainfall, leading to distorted calculations of cumulative water consumption and thus affecting the scientific validity of irrigation decisions.

[0024] Furthermore, existing technologies lack quantifiable means to establish a correlation between user behavior and plant growth status. Even if students participate in watering, they cannot intuitively perceive the impact of their actions on plant health, leading to a decline in their willingness to participate over time. Although a few educational applications attempt to incentivize students through points and badges, these mechanisms are disconnected from real plant physiological responses and are essentially driven by external rewards, making it difficult to generate sustained, intrinsic motivation for participation. More importantly, existing systems lack conflict prevention mechanisms to support multi-user collaboration. When multiple students are assigned to the same green area, duplicate watering or even over-watering can easily occur, wasting water resources and potentially harming plant growth.

[0025] From a technical architecture perspective, current systems generally lack a closed-loop processing framework that integrates environmental perception, user interaction, water usage accounting, and visual feedback. Specifically, they lack the ability to jointly analyze historical meteorological data (especially daily rainfall) and user-submitted irrigation commands, making it impossible to accurately estimate the total amount of water actually received by the plants; they lack a water usage-growth mapping model based on plant species characteristics, making it difficult to transform abstract water usage data into understandable growth status indicators; and they lack a technical path to analyze the deviation between the actual growth status and the ideal reference curve and generate corrective feedback. These problems prevent the system from providing students with dynamically updated visual feedback that is strongly correlated with their operational behavior, thus limiting the in-depth application of intelligent irrigation systems in educational settings.

[0026] In summary, existing technologies for campus greening management targeting students suffer from several shortcomings, including inefficient use of environmental data, superficial user interaction, a disconnect between user behavior and plant status, and a lack of multi-user collaboration mechanisms. These shortcomings cannot be resolved simply by increasing public awareness or manual scheduling; rather, they stem from a lack of key underlying technology modules such as multi-source data fusion, dynamic water usage calculation, growth status modeling, and linked visual feedback. A new technological solution is urgently needed to overcome these deficiencies.

[0027] To address the aforementioned issues, this application constructs an interactive method and system for campus green plant irrigation management targeting student users. By dividing the green area into multiple target irrigation zones and establishing a binding relationship with students, students can submit instructions containing irrigation water volume and frequency via mobile terminals. The system integrates historical meteorological data of the area, especially rainfall information, to accurately calculate the cumulative water consumption. Based on a preset mapping relationship between plant type, water consumption, and growth status, it generates a real-time growth curve reflecting the current growth status of the plants. Furthermore, by combining a reference planting curve and a correction factor derived from user behavior data, it calculates the growth deviation, thereby dynamically driving the visual morphological changes of the three-dimensional virtual green plant model. This achieves a closed-loop linkage between environmental perception, user operation, water consumption statistics, growth modeling, and visual feedback at the technical level, effectively solving key technical problems in existing systems such as extensive data utilization, shallow interaction, disconnect between behavior and plant status, and lack of multi-user collaboration support.

[0028] First, the management and interaction method for watering campus green plants for students provided in this application will be described in detail below with reference to the accompanying drawings.

[0029] Reference Figure 1 The implementation process of the student-oriented campus green plant watering management interaction method provided in this application embodiment includes, but is not limited to, the following steps.

[0030] Step S110: Divide the green areas on campus into multiple target irrigation areas and establish a binding relationship between each target irrigation area and one or more students.

[0031] In step S110, the green areas on campus are divided into multiple target irrigation zones, and a binding relationship is established between each target irrigation zone and one or more students, providing a structured foundation for subsequent personalized interactions and responsibility assignment. By clearly defining management units through spatial division, each student has only operational permissions and feedback associated with their assigned zone, thus avoiding operational conflicts or resource waste caused by multiple students interfering in the same zone in a disorderly manner. This also lays the data organization prerequisite for the system to achieve zone-based water usage statistics, behavior tracking, and the generation of individualized feedback.

[0032] Step S120: Provide students with a watering interactive interface through a mobile terminal device with an installed watering interactive application. The watering interactive interface includes watering control operation controls to receive watering instructions input by students for their bound target watering area.

[0033] The irrigation instructions include at least the amount of irrigation water, the frequency of irrigation, and the irrigation time.

[0034] In step S120, a digital operation channel is constructed between students and physical green plants, enabling non-professional users to express their maintenance intentions in an intuitive and controllable way. This transforms the originally vague "watering" behavior into structured digital instructions, providing standardized input for the system to perform water usage accounting, behavior analysis, and effect evaluation in the future.

[0035] Step S130: Obtain historical meteorological data corresponding to the target irrigation area.

[0036] In step S130, external environmental variables are introduced as an objective reference for irrigation decisions. In particular, by collecting actual meteorological records of the area during historical periods, the system can identify the replenishment effect of natural precipitation on soil moisture, thereby avoiding the separation of artificial irrigation from natural rainfall and providing necessary data support for subsequent accurate calculation of the total amount of water actually received by the plants.

[0037] Step S140: Based on the irrigation instructions input by the students and the rainfall information in the historical meteorological data, the cumulative water consumption of the target irrigation area is calculated.

[0038] In step S140, data from both human intervention and natural supply are integrated. The planned irrigation instructions submitted by students are quantitatively superimposed with the rainfall events that have occurred to form a cumulative value that reflects the actual total amount of water received by the plants. This overcomes the bias in water use assessment caused by the existing system that only counts artificial water use and ignores natural replenishment, and improves the accuracy of subsequent growth status projection.

[0039] Step S150: Based on the preset mapping table of plant species-water consumption-growth status, generate a real-time growth curve representing the current growth status of the plant according to the cumulative water consumption.

[0040] In step S150, the abstract water usage data is transformed into biologically meaningful growth status indicators. By using a built-in prior knowledge model of different plant responses to water, the system can dynamically infer the physiological stage or health level of the plant based on the current cumulative water usage and present its growth trend in the form of a continuous curve, providing a comparable real-time benchmark for subsequent deviation analysis.

[0041] Step S160: Based on the real-time growth curve, the preset reference planting curve, and the correction factor determined based on user behavior data, the growth deviation is calculated.

[0042] In step S160, the difference between the current growth status and the ideal status is quantified by comparing multi-dimensional data. The reference planting curve represents the expected growth trajectory of the plant under standard maintenance conditions, while the correction factor is used to compensate for the systematic deviation caused by the students' operating habits. This makes the deviation not only reflect the influence of the environment and water use, but also incorporate user behavior characteristics, thereby improving the adaptability and personalization of the evaluation results.

[0043] Step S170: Based on the growth deviation, dynamically adjust the visualization of the three-dimensional virtual green plant model displayed on the mobile terminal device that corresponds to the target irrigation area.

[0044] In step S170, the complex growth assessment results are transformed into intuitive and immediate visual feedback. By changing the visual attributes of the virtual model, such as height, leaf density, color saturation, or overall shape, students can perceive the actual effect of their maintenance behavior without understanding the underlying data. This establishes a strong cognitive link between operation and result, enhancing the immersion of participation and the effectiveness of feedback.

[0045] In some embodiments of this application, step S110 involves dividing the green area on campus into multiple target irrigation areas and establishing a binding relationship between each target irrigation area and one or more students, including the following steps.

[0046] Step S210: Pre-configure multiple target irrigation areas and their corresponding plant species information.

[0047] In step S210, a basic data framework is established for the entire irrigation management interactive system. By clearly defining the spatial units of the campus green area during the system initialization phase and binding specific plant species attributes to each unit, the subsequent water use calculation, growth status modeling, and visualization feedback can be differentiated based on the physiological characteristics of the plants themselves, thereby ensuring that the system operation has biological rationality and technical operability.

[0048] Step S220: In response to the adoption request initiated by the student through the mobile terminal device, a list of target irrigation areas that can be adopted is pushed to the student.

[0049] In step S220, a connection mechanism is established between the system's preset resources and the user's active selection. By receiving the student's adoption intention and dynamically returning the currently available area options, the student can exercise their right to choose within a limited and clear scope. This not only ensures the orderly allocation of resources but also enhances the user's initiative and sense of belonging. At the same time, it provides triggering conditions for the establishment of subsequent binding relationships.

[0050] Step S230: Receive the target irrigation area selected by the student from the list, and assign the target irrigation area to a student group consisting of one or more students. At the same time, synchronize the plant species, ideal growth cycle and reference planting curve of the target irrigation area to the student group members.

[0051] In step S230, the target irrigation area is formally bound to the specific user group, and key plant maintenance benchmark information is pushed to all relevant members at the same time the binding takes effect. This enables team members to understand the basic characteristics and expected growth trajectory of the plants they are responsible for before operation, providing the necessary cognitive premise and technical context for understanding system feedback and adjusting irrigation behavior.

[0052] In some embodiments of this application, the correction factor determined based on user behavior data is generated according to the following steps.

[0053] Step S310: After the student sets the irrigation time, obtain the forecast meteorological data for the target irrigation area for the next 24 hours. The forecast meteorological data includes rainfall, daily maximum temperature, daily minimum temperature, and relative humidity.

[0054] In step S310, forward-looking environmental variables are introduced to provide dynamic prediction of the upcoming plant water demand. By capturing key meteorological parameters for the next day in real time after the user submits the irrigation plan, the system can evaluate the rationality of the current irrigation decision based on the upcoming weather conditions, thus laying a data foundation for generating scientific water use recommendations.

[0055] Step S320: Based on the forecast meteorological data, calculate the effective water requirement correction coefficient for plants on the day, and generate a recommended irrigation amount accordingly, which is displayed on the mobile terminal device for students' reference.

[0056] In step S320, abstract meteorological information is transformed into specific and operable irrigation guidance. By combining plant physiological response models with future weather trends, the theoretical water requirement is dynamically adjusted and personalized recommended values ​​are output. This enables non-professional student users to obtain technical assistance when making decisions, reducing the risk of over- or under-irrigation due to lack of experience. At the same time, it provides a standard reference for the system to record user behavior deviations.

[0057] Step S330: Record the deviation ratio between the water volume finally confirmed by the student and the recommended water volume, and use this deviation ratio as a correction factor determined based on user behavior data for subsequent calculation of growth deviation.

[0058] In step S330, the difference between the user's actual operation and the system's suggestion is quantified, and this difference is solidified into a reusable behavioral characteristic parameter. This allows the subsequent assessment of plant growth status to not only reflect objective water use and environmental factors, but also incorporate the operational preferences or cognitive biases of specific user groups, thereby improving the accuracy and personalization of growth deviation calculation.

[0059] In some embodiments of this application, the effective water demand correction factor Calculate according to the following formula (1): (1); In formula (1), Forecast rainfall for the next 24 hours, The maximum daily evapotranspiration requirement of the plants planted in the target irrigation area at the current growth stage is obtained from a table using a reference planting curve.

[0060] Formula (1) is used to calculate the effective water demand correction factor. Its function is to determine the amount of rainfall forecast for the next 24 hours. The maximum daily transpiration demand of the plants planted in the target irrigation area at the current growth stage The system dynamically adjusts the assessment benchmark for actual plant water requirements based on the relationship between the forecast rainfall and the actual water demand. When the forecast rainfall is less than the maximum daily evapotranspiration demand, the system considers natural precipitation insufficient to meet the plant's water needs. In this case, the correction coefficient is 1 minus the rainfall percentage, reflecting the remaining water demand. Conversely, when the forecast rainfall is greater than or equal to the maximum daily evapotranspiration demand, the system determines that natural precipitation is sufficient to meet the daily water requirement, and the correction coefficient is zero, indicating that no additional artificial irrigation is needed. This formula enables the effective quantitative utilization of natural precipitation, avoiding unnecessary watering during rainy weather, thereby improving water resource utilization efficiency and enhancing the scientific basis of irrigation decisions.

[0061] In some embodiments of this application, the recommended irrigation water volume is... Generate according to the following formula (2): (2); In formula (2), This is the standard daily watering amount for the plant at its current growth stage.

[0062] Formula (2) is used to generate the recommended irrigation water volume. Its function is based on the standard daily irrigation amount for plants at their current growth stage. The effective water demand correction factor calculated by formula (1) Multiplying these values ​​yields a personalized irrigation recommendation based on environmental conditions. This formula combines theoretical standard water usage with real-time weather forecasts, allowing the recommended water volume to be dynamically adjusted according to weather changes. This ensures a reasonable water supply to plants while reducing the risk of students blindly operating the system due to a lack of professional knowledge. By displaying this recommended value to users, the system can provide technical guidance without interfering with their autonomy, achieving intelligent assisted decision-making and improving the overall accuracy of irrigation management and user experience.

[0063] In some embodiments of this application, historical meteorological data includes the actual daily rainfall over the past 7 days. Step S140 involves calculating the cumulative water consumption of the target irrigation area based on the irrigation instructions input by the student and the rainfall information from the historical meteorological data, including the following steps.

[0064] Step S410: The effective historical rainfall is obtained by weighted summation of the actual daily rainfall over the past 7 days, with the weights calculated in reverse chronological order using an exponential decay function. Perform calculations, where It represents the number of days from the current date. The soil moisture dissipation coefficient ranges from 0.1 to 0.5.

[0065] In step S410, the actual contribution of historical rainfall to the current soil moisture content is scientifically quantified. Considering that natural precipitation will gradually evaporate or infiltrate and be lost in the soil over time, the earlier the rainfall, the smaller the impact on the current available water for plants. Therefore, an exponential decay function is introduced to assign different weights to the daily rainfall, so that recent rainfall has a higher influence, thereby more realistically reflecting the current effective water status of the soil and avoiding the distortion of water use assessment caused by accumulating all historical rainfall in equal amounts.

[0066] Furthermore, an exponential decay function is used to weight and sum the actual daily rainfall over the past 7 days in reverse chronological order. This simulates the dynamic process of soil moisture dissipation over time, ensuring that the impact of historical rainfall on current available water for plants decreases non-linearly with respect to its timing. Since soil moisture gradually decreases due to evaporation, infiltration, and plant absorption, rainfall closer to the current date contributes more to soil moisture content, while the impact of earlier rainfall gradually weakens. By introducing an exponential decay weight function with the number of days from the current date as the variable, the system can scientifically quantify this time-dependent water retention effect, avoiding overestimation caused by equal summation of all historical rainfall. This more accurately reflects the current effective water holding capacity of the soil, improving the accuracy and ecological rationality of cumulative water consumption calculations.

[0067] Step S420: Add the irrigation water volume input by the student to the effective historical rainfall to obtain the cumulative water consumption. If the actual daily rainfall exceeds the maximum daily water absorption threshold of the roots of the plants planted in the target irrigation area, the excess portion is not included in the effective historical rainfall.

[0068] In step S420, the two types of water sources, human intervention and natural replenishment, are integrated to form a unified comprehensive index that reflects the total amount of water actually received by the plants. By adding the planned or executed irrigation water volume to the effective historical rainfall volume after time decay correction, the system can accurately calculate the total amount of available water accumulated in the target irrigation area at the current time point. This provides a reliable data foundation for subsequent projections of plant growth status based on water consumption, ensuring that growth assessment considers both artificial maintenance behaviors and fully incorporates environmental natural supply factors.

[0069] Specifically, if the actual daily rainfall exceeds the maximum daily water absorption threshold of the roots of the plants planted in the target irrigation area, the excess portion is not included in the effective historical rainfall. This limitation aims to eliminate the interference of ineffective rainfall on water use statistics. Plant roots have a physiological upper limit to their ability to absorb water per unit time. When daily rainfall exceeds this threshold, the excess water cannot be effectively utilized by the plants and is usually lost through surface runoff or deep infiltration. Including all of this in the effective rainfall would overestimate the actual available soil water, leading to biased subsequent irrigation decisions. By setting this cutoff mechanism, the system ensures that the effective historical rainfall always falls within the plant's absorbable range, significantly improving the biological rationality and technical accuracy of cumulative water use calculations.

[0070] In some embodiments of this application, step S160 involves calculating the growth deviation based on the real-time growth curve, a preset reference planting curve, and a correction factor determined based on user behavior data, including the following steps.

[0071] Step S510: Under the same time coordinate, calculate the integral area of ​​the real-time growth curve and the preset reference planting curve within the preset observation period, and use the ratio of the integral area of ​​the real-time growth curve to the integral area of ​​the reference planting curve as the basic growth deviation.

[0072] In step S510, the overall difference between the actual growth state of the plant and the ideal growth trajectory is quantified by mathematical integration, avoiding the bias or random errors caused by relying solely on instantaneous values ​​at a single point in time. Since growth is a continuous and cumulative process, using the integrated area can more comprehensively reflect the overall growth performance of the plant throughout the entire observation period. Defining the ratio of the two areas as the basic growth deviation provides a dimensionless and comparable standardized indicator, laying an objective foundation for subsequent fine-tuning by incorporating user behavior factors.

[0073] Step S520: Use the correction factor to perform a weighted adjustment on the basic growth deviation to obtain the final growth deviation.

[0074] In step S520, the actual operating habits and behavioral preferences of the student group are integrated into the growth assessment system. This ensures that the deviation not only reflects objective differences caused by the environment and water usage, but also reflects the systematic impact of user behavior patterns on plant care effectiveness. The correction factor is derived from the deviation ratio between the water volume confirmed by the student and the system's recommended value. It represents the consistency tendency of a specific user group in decision-making. By using this factor as a weight to adjust the basic deviation, the system can generate more targeted and adaptive assessment results, thereby improving the accuracy and educational effectiveness of subsequent visual feedback and interactive guidance.

[0075] In some embodiments of this application, a plant growth morphology display area is provided on the mobile terminal device for rendering a three-dimensional virtual green plant model. Step S170 involves dynamically adjusting the visualization of the three-dimensional virtual green plant model displayed on the mobile terminal device, corresponding to the target irrigation area, based on the growth deviation, including the following steps.

[0076] Step S610: A multi-level virtual morphology library is pre-constructed for each plant type, with each level corresponding to a preset growth deviation range.

[0077] In step S610, a mapping relationship between plant visual appearance and growth status is established. By designing a series of differentiated 3D model morphologies for different plant species, each possible degree of growth deviation can be mapped to a concrete and intuitive visual expression. This hierarchical modeling approach ensures that subsequent visual feedback not only conforms to the plant's biological characteristics but also clearly conveys the current state of care, providing students with understandable and perceptible status prompts, thereby enhancing the realism and educational significance of the interaction.

[0078] Step S620: Based on the currently calculated growth deviation, match the corresponding morphological level and render the corresponding level of 3D virtual green plant model in the display area.

[0079] In step S620, the growth assessment results are converted into visual feedback in real time. By accurately mapping the numerical deviation to a predefined morphological level, the system can dynamically update the appearance of the virtual green plant displayed on the mobile terminal, so that its visual attributes such as height, leaf density, branch thickness, or overall lushness change synchronously with the actual growth status of the plant. This instantaneous and continuous visualization mechanism allows students to intuitively perceive the impact of their watering behavior on plant health without interpreting complex data, effectively establishing a cognitive loop between operation and result.

[0080] Step S630: When the growth deviation is higher than the preset warning value for three consecutive days, a withering animation effect is superimposed on the virtual model, and a maintenance reminder message is pushed to the mobile terminal device.

[0081] In step S630, a reinforced warning mechanism for abnormal states is introduced. By setting time accumulation conditions, misjudgments caused by single-day fluctuations are avoided, and intervention measures are only triggered under continuous poor maintenance conditions. At this time, the system not only overlays withering animations such as leaf curling, yellowing, or drooping branches on a visual level to enhance emotional resonance, but also actively pushes text reminder messages to guide students to re-examine their watering strategies. Thus, at the technical level, it achieves an upgrade from passive display to active guidance, improving the system's educational intervention capabilities and user response efficiency.

[0082] In some embodiments of this application, the method further includes: in response to a real-scene photo of plants in a target irrigation area uploaded by a student via a mobile terminal device, comparing and displaying the real-scene photo with the plant growth status predicted based on the cumulative water consumption, which is achieved through the following steps.

[0083] Step S710: Use a pre-trained convolutional neural network model to estimate the leaf area index or plant height of the real-world photo.

[0084] In step S710, the plant images uploaded by students are converted into quantifiable growth indicators. Using deep learning technology, key parameters with agronomical significance, such as leaf area index or plant height, are automatically extracted from the unstructured visual data. This provides objective and digital evidence for subsequent comparison with system predictions. This process avoids the subjectivity and operational barriers of manual measurement, allowing ordinary student users to easily contribute real growth data and enhancing the closed-loop nature and authenticity of the system's feedback.

[0085] Step S720: Perform a deviation analysis between the estimated results and the theoretical growth indicators predicted based on the current cumulative water consumption and the reference planting curve.

[0086] In step S720, a quantitative comparison mechanism is established between real observations and model predictions. By calculating the differences between the actual growth parameters extracted from real-world photos by a convolutional neural network and the theoretical values ​​extrapolated by the system based on water usage history and ideal growth models, it is possible to identify whether the actual plant development is lagging, ahead of schedule, or in line with expectations. This deviation analysis not only verifies the accuracy of the system's water usage-growth mapping model but also provides students with a third-party verification of the effectiveness of their maintenance behaviors, strengthening the consistency between virtual feedback and physical reality.

[0087] Step S730: Display the real-world photo and the 3D virtual plant model in a side-by-side view on the mobile terminal device, and label the quantitative differences between the two in terms of plant height, crown width, or leaf density.

[0088] In step S730, intuitive visual comparisons enhance students' cognitive understanding and reflective abilities, transforming abstract numerical deviations into concrete and identifiable morphological differences. The side-by-side layout allows users to simultaneously observe the state of real plants and virtual models, while the labeled quantitative differences clearly indicate inconsistencies in dimensions such as plant height, crown width, or leaf density. This helps students identify shortcomings or misconceptions in plant care, thereby prompting them to adjust their subsequent watering behavior and achieving a complete educational loop from perception to understanding to action.

[0089] In some embodiments of this application, the system guides students to regularly take and upload real-world photos of the plants they are cultivating by sending push notifications containing encouraging language. Computer vision algorithms are used to automatically extract key growth indicators such as plant height, number of leaves, or crown width, and these are then visually compared with the theoretical growth predicted by a reference growth curve generated based on cumulative water consumption, plant species, and environmental data. When the actual growth status continuously deviates from the predicted curve, the system analyzes possible causes by combining historical watering records, local weather data, and plant physiological characteristics, and generates easy-to-understand improvement suggestions, such as reducing watering frequency to cope with recent rainfall or adjusting light conditions to promote healthy growth. This function guides students to continuously reflect on and iterate their cultivation methods in practice, cultivating their scientific observation and causal reasoning abilities.

[0090] In some embodiments of this application, the mobile terminal device also provides a communication community module, allowing students bound to different target irrigation areas to upload textual descriptions of their irrigation experiences and plant growth records. This creates a practice-based collaborative learning platform, enabling students to share their operational insights, observations, and reflections on problems accumulated during plant care in text or graphic form, thereby promoting the flow of experience and knowledge accumulation across regions and groups. By encouraging users to actively record and publicly share the correlation between their care behaviors and plant responses, the system not only enhances students' sense of participation and responsibility but also provides real-world case studies for group learning, effectively compensating for the limitations of single virtual feedback in terms of contextual diversity and further enriching the dimensions of educational interaction.

[0091] In some embodiments of this application, the communication community module is further configured with a knowledge-based question-and-answer submodule. This submodule is associated with a pre-built plant care knowledge graph. When a student asks a question about plant growth in the communication community, the system automatically retrieves matching causal analyses and treatment suggestions from the knowledge graph and pushes them to the mobile terminal devices of the student asking the question and their student group in the form of structured cards. Therefore, by transforming unstructured user questions into accurate and reliable technical support services, and relying on the pre-built plant care knowledge graph, the system can understand the semantics of the question and quickly locate the corresponding physiological or environmental causes, thereby generating targeted diagnostic conclusions and operational suggestions. Presenting the answers in the form of structured cards not only improves the readability and professionalism of the information but also allows for simultaneous push to the entire student group, enabling collaborative knowledge acquisition and collective learning. This technically supports an intelligent, responsive, and educational plant care assistance mechanism.

[0092] In some embodiments of this application, the system anonymizes and aggregates the maintenance behavior data of all students in a class without exposing individual identities, constructing a lightweight virtual plant community. When a student continuously completes basic watering tasks, not only is their personal achievement badge updated, but it also triggers positive changes in the group's shared virtual environment, such as increased collective forest brightness, soothing background music, or unlocking new ecological scenes. Furthermore, the system uses artificial intelligence to analyze the operating patterns of inactive students and automatically generates personalized social invitation messages, such as "Your mint misses you, come see it with Xiaoming," pushed from the peer's account, leveraging the gentle peer effect to encourage participation. This mechanism effectively maintains long-term interest and fosters a positive group atmosphere while ensuring data privacy.

[0093] In some embodiments of this application, the system obtains students' course schedules and exam cycles through an interface with the school's academic affairs platform, or receives students' self-marked stress levels, or uses heart rate variability data collected by wearable devices to assess their current academic stress level in real time. When a student is determined to be in a high-stress phase, the system automatically reduces the frequency of watering task reminders, simplifies the interactive interface operation steps, and adjusts the feedback tone of the virtual plant to a soothing and encouraging tone; while in low-stress phases, exploratory interactive content is appropriately introduced, such as small plant growth experiments or environmental variable simulation tasks. This mechanism dynamically adapts to the user's cognitive load, preventing maintenance activities from becoming an additional burden at critical moments, and achieving intelligent coordination between educational pace and psychological state.

[0094] In some embodiments of this application, during the watering interaction using a mobile terminal, the system captures the user's facial micro-expressions through the device's front-facing camera or analyzes the emotional tendency in their voice tone through the microphone, using a pre-trained emotion recognition model to determine the user's current emotional state. This emotional information, along with the watering behavior, serves as input and participates in determining the morphological evolution logic of the virtual plant according to dynamic weights. For example, even if the student does not perform the watering operation that day, but the system detects that they are in a low mood, the virtual plant will still exhibit a gentle growth posture and trigger comforting text such as "I'm here with you" or soft lighting effects as feedback. Thus, the green plant is no longer just an object to be cared for, but becomes a companion with contextual awareness and emotional responsiveness, enhancing the emotional warmth of human-computer interaction.

[0095] Secondly, refer to Figure 2 This application provides a management and interactive system for watering campus greenery for students, including a central control terminal deployed on a campus server or cloud platform and multiple mobile terminal devices with watering interactive applications installed. The system achieves intelligent and educational integration of campus greenery maintenance through a distributed collaborative mechanism. The central control terminal, as the core data processing and logic execution unit, undertakes key functions such as global resource scheduling, environmental data fusion, water usage calculation, growth status modeling, and feedback generation, ensuring technical consistency and computational reliability throughout the watering management process. The mobile terminal devices serve as the main interaction entry point for student users, supporting individualized functions such as inputting operation commands, visualizing virtual models, and uploading real-world images. They also support community communication and knowledge acquisition, thus constructing a complete closed-loop system from perception and decision-making to feedback and collaboration at the technical level.

[0096] The central control unit is configured to manage multiple target irrigation areas on campus and execute the aforementioned student-oriented campus green plant irrigation management and interaction method; the mobile terminal device is configured to provide an irrigation interaction interface, display a three-dimensional virtual green plant model, upload real-scene photos, and participate in community interaction.

[0097] Specifically, the central control terminal is configured to centralize the complex logic involved in the aforementioned methods, such as data collection, binding relationship maintenance, cumulative water consumption statistics, growth deviation calculation, and correction factor application. Relying on the computing and storage capabilities of servers or cloud platforms, it efficiently supports real-time response needs in multi-region, multi-user concurrent scenarios. At the same time, it ensures the unified management and secure access to core resources such as plant species information, reference planting curves, meteorological data interfaces, and knowledge graphs, providing a stable and scalable technical foundation for the entire system.

[0098] Furthermore, mobile devices are configured to transform the complex logic of plant care into a lightweight, interactive experience suitable for students. Intuitive controls lower the barrier to entry, dynamically updated 3D virtual models establish a visual connection between actions and results, uploading real-world photos enables two-way verification between the physical world and the digital system, and a community module promotes experience sharing and knowledge exchange. This user-centric design, centered on mobile devices, not only enhances the system's accessibility and engagement but also ensures that technology truly serves educational goals, transforming plant care from a simple task into an immersive learning experience.

[0099] In summary, the management and interaction method and system for watering campus green plants for students provided in this application have the following technical effects.

[0100] This application achieves a structured allocation of maintenance responsibilities by dividing the campus green area into multiple target irrigation zones and establishing a binding relationship with student users. By integrating irrigation commands input by students with rainfall information from historical meteorological data, it accurately calculates the cumulative water consumption, overcoming the water consumption assessment bias caused by traditional systems ignoring natural precipitation or making rough estimates. Based on the mapping relationship between plant species, water consumption, and growth status, it generates real-time growth curves and calculates the growth deviation by combining reference planting curves and user behavior correction factors, making the assessment of plant growth status both biologically reasonable and adaptable to user behavior. Furthermore, by dynamically adjusting the visualization of the three-dimensional virtual green plant model, it transforms the abstract growth assessment results into intuitive and immediate visual feedback, effectively establishing a cognitive closed loop between student operation behavior and plant response.

[0101] Meanwhile, the system supports uploading real-scene photos and quantitatively comparing virtual models, and introduces convolutional neural networks to automatically estimate real growth indicators, enhancing the authenticity and verification capabilities of the feedback. The integration of the communication community module and the knowledge Q&A sub-module achieves a collaborative mechanism for experience sharing and intelligent knowledge push at the technical level, improving the system's educational support capabilities. Overall, the method and system architecture achieves deep integration of environmental perception, user interaction, water usage accounting, growth modeling, visual feedback, and social collaboration, solving key technical problems in existing technologies such as extensive data utilization, shallow interaction, disconnect between behavior and plant status, and lack of multi-person collaboration support. It significantly improves the intelligence level, resource utilization efficiency, and effectiveness of student participation in campus green plant irrigation management.

[0102] It should be noted that in all specific embodiments of this application, all data processing activities related to user identity or personal characteristics, such as user information, user behavior data, historical data, and location information, will be conducted in accordance with the principles of legality, legitimacy, and necessity. All data collection, use, storage, and processing will be subject to compliance with applicable national and regional laws, regulations, and industry standards, and informed consent from users will be obtained in a clear and explicit manner before processing. For the processing of sensitive personal information, separate consent from users will be obtained through prominent means such as pop-up prompts and independent confirmation pages. If any processing conflicts with laws and regulations, the laws and regulations will prevail, and necessary data processing will only be carried out within the scope permitted by laws and regulations, ensuring that all data-based applications, analyses, and technical implementations are conducted within the scope permitted by laws and regulations.

[0103] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0104] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of ordinary skill of an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary skill. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0105] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part 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 programs 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 of the various embodiments of this invention. 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.

[0106] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0107] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Additionally, computer-readable media can even be paper or other suitable media on which programs can be printed, for example, by optically scanning the paper or other media, then editing, interpreting, or, if necessary, processing it in a suitable manner to obtain the program electronically, and then storing it in computer memory.

[0108] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0109] In the foregoing description of this specification, the reference to terms such as "one embodiment / implementation," "another embodiment / implementation," or "certain embodiments / implementations," etc., indicates that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in an embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0110] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0111] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A student-oriented management and interaction method for watering campus green plants, characterized in that, include: The green areas on campus are divided into multiple target irrigation zones, and each target irrigation zone is associated with one or more students. A watering interactive interface is provided to students through a mobile terminal device with an installed watering interactive application. The watering interactive interface includes watering control operation controls to receive watering instructions input by students for their bound target watering area. The watering instructions include at least the water volume, watering frequency, and watering time. Obtain historical meteorological data corresponding to the target irrigation area; Based on the irrigation instructions input by the students and the rainfall information in the historical meteorological data, the cumulative water consumption of the target irrigation area is calculated. Based on a preset mapping table of plant species, water consumption, and growth status, a real-time growth curve representing the current growth status of the plant is generated according to the cumulative water consumption. The growth deviation is calculated based on the real-time growth curve, the preset reference planting curve, and the correction factor determined based on user behavior data. Based on the growth deviation, the visualization of the three-dimensional virtual green plant model displayed on the mobile terminal device, corresponding to the target irrigation area, is dynamically adjusted.

2. The student-oriented campus green plant watering management and interaction method according to claim 1, characterized in that, The process of dividing the green areas on campus into multiple target irrigation zones and establishing a binding relationship between each target irrigation zone and one or more students includes: Pre-configure multiple target irrigation areas and their corresponding plant species information; In response to a student's adoption request initiated through the mobile terminal device, a list of target irrigation areas that can be adopted is pushed to the student; The system receives the target irrigation area selected by the student from the list and assigns the target irrigation area to a student group consisting of one or more students. At the same time, the system synchronizes the plant species, ideal growth cycle and reference planting curve of the target irrigation area with the student group members.

3. The student-oriented campus green plant watering management and interaction method according to claim 1, characterized in that, The correction factor determined based on user behavior data is generated according to the following steps: After the students set the watering time, the forecast meteorological data of the target watering area for the next 24 hours is obtained. The forecast meteorological data includes rainfall, daily maximum temperature, daily minimum temperature and relative humidity. Based on the forecast meteorological data, the effective water requirement correction coefficient for plants on that day is calculated, and a recommended irrigation amount is generated accordingly, which is displayed on the mobile terminal device for students' reference. Record the deviation ratio between the water volume finally confirmed by the student and the recommended water volume, and use this deviation ratio as a correction factor determined based on user behavior data for subsequent calculation of growth deviation.

4. The student-oriented campus green plant watering management and interaction method according to claim 3, characterized in that, The effective water demand correction coefficient Calculate using the following formula: ； in, Forecast rainfall for the next 24 hours, The maximum daily evapotranspiration requirement of the plants planted in the target irrigation area at the current growth stage is obtained by referring to the table from the reference planting curve; The recommended irrigation water volume Generate using the following formula: ； in, This is the standard daily watering amount for the plant at its current growth stage.

5. The student-oriented campus green plant watering management and interaction method according to claim 1, characterized in that, The historical meteorological data includes the actual daily rainfall over the past 7 days; The step of calculating the cumulative water consumption of the target irrigation area based on the irrigation instructions input by the students and the rainfall information in the historical meteorological data includes: The effective historical rainfall is obtained by weighting and summing the actual daily rainfall over the past 7 days, with the weights arranged in reverse chronological order using an exponential decay function. Perform calculations, where It represents the number of days from the current date. The soil moisture dissipation coefficient ranges from 0.1 to 0.

5. The cumulative water consumption is obtained by adding the irrigation water volume entered by the student to the effective historical rainfall. If the actual daily rainfall exceeds the maximum daily water absorption threshold of the roots of the plants planted in the target irrigation area, the excess portion will not be included in the effective historical rainfall.

6. The student-oriented campus green plant watering management and interaction method according to claim 1, characterized in that, The calculation of growth deviation based on the real-time growth curve, the preset reference planting curve, and the correction factor determined based on user behavior data includes the following steps: Under the same time coordinate, the integral area of ​​the real-time growth curve and the preset reference planting curve within the preset observation period is calculated, and the ratio of the integral area of ​​the real-time growth curve to the integral area of ​​the reference planting curve is used as the basic growth deviation. The baseline growth deviation is weighted and adjusted using the correction factor to obtain the final growth deviation.

7. The student-oriented campus green plant watering management and interaction method according to claim 1, characterized in that, The mobile terminal device is equipped with a plant growth morphology display area for rendering the three-dimensional virtual green plant model; The step of dynamically adjusting the visualization of the three-dimensional virtual green plant model displayed on the mobile terminal device, corresponding to the target irrigation area, based on the growth deviation includes: A multi-level virtual morphology library is pre-built for each plant type, with each level corresponding to a preset growth deviation range; Based on the currently calculated growth deviation, the corresponding morphological level is matched, and a three-dimensional virtual green plant model of the corresponding level is rendered in the display area. When the growth deviation exceeds the preset warning value for three consecutive days, a withering animation effect is superimposed on the virtual model, and a maintenance reminder message is pushed to the mobile terminal device.

8. The student-oriented campus green plant watering management and interaction method according to claim 1, characterized in that, The method further includes: in response to a real-scene photo of the plants in the target irrigation area uploaded by a student through the mobile terminal device, comparing and displaying the real-scene photo with the plant growth status predicted based on the cumulative water consumption; The comparison presentation includes: The leaf area index or plant height of the real-world photos is estimated using a pre-trained convolutional neural network model. The estimated results were compared with the theoretical growth indicators predicted based on the current cumulative water consumption and the reference planting curve. The real-world photo and the three-dimensional virtual plant model are displayed side-by-side on the mobile terminal device, and the quantitative differences between the two in terms of plant height, crown width, or leaf density are marked.

9. The student-oriented campus green plant watering management and interaction method according to claim 1, characterized in that, The mobile terminal device also provides a communication community module, which allows students who are bound to different target irrigation areas to upload irrigation experience texts and plant growth records in the communication community; The communication community module is also equipped with a knowledge Q&A sub-module, which is associated with a pre-built plant care knowledge graph. When a student asks a question about plant growth in the communication community, the system automatically retrieves matching cause analysis and treatment suggestions from the knowledge graph and pushes them to the mobile terminal devices of the student who asked the question and the student group in the form of structured cards.

10. A student-oriented management and interactive system for watering campus greenery, characterized in that, This includes a central control terminal deployed on a campus server or cloud platform and multiple mobile terminal devices with irrigation interactive applications installed. The central control terminal is configured to manage multiple target irrigation areas within the campus and execute the student-oriented campus green plant irrigation management interaction method as described in any one of claims 1 to 9. The mobile terminal device is configured to provide an interactive watering interface, display a 3D virtual green plant model, upload real-world photos, and participate in community interactions.

Images