Multi-area greening seedling irrigation collaborative management platform for urban greening seedling planting

Abstract

The invention discloses a multi-area greening seedling irrigation collaborative management platform for urban greening seedling planting, and relates to the field of urban greening irrigation. The distributed environment sensing module collects vegetation and environment parameters through multiple sensors and the unmanned aerial vehicle; the plant growth model library integrates a plant water demand rule to optimize irrigation; the multi-objective optimization decision module generates an optimal scheme based on an algorithm; the intelligent irrigation execution system adopts a bionic pipe network, an intelligent nozzle and an internet-of-things valve precise control small unit; the block chain traceability management module constructs a double-chain parallel architecture to store and share data; the man-machine interaction decision module improves the management efficiency through a meta universe platform, an intelligent assistant and a VR scene. The water-saving performance is outstanding, the vegetation coverage rate is increased, and plant diseases and insect pests are reduced; the annual operation cost is reduced, the maintenance cost is reduced, and the training period is shortened; rainwater recycling is supported, the carbon sink amount is increased, the landscape satisfaction degree is improved, the public complaint rate is reduced, and the win-win effect of economic benefits and ecological benefits is achieved.

Description

Technical Field

[0001] This invention relates to the field of urban greening irrigation technology, and in particular to a multi-regional greening seedling irrigation collaborative management platform for urban greening seedling planting. Background Technology

[0002] Urban greening irrigation currently faces multiple technical bottlenecks, hindering the efficient use of water resources and the sustainable development of the ecological environment. Traditional irrigation systems rely on human experience for decision-making, lacking the dynamic perception of plant water requirements and environmental changes, resulting in a large deviation rate in irrigation volume. In addition, existing irrigation equipment is mostly extensive spraying, unable to implement precise irrigation for different plant species and growth stages, causing over-irrigation in some areas leading to soil compaction, and water shortage in other areas affecting plant survival rates.

[0003] While intelligent irrigation technology has begun to emerge, it suffers from low system integration and severe data silos. Existing solutions often rely on single sensors to monitor soil moisture, neglecting crucial factors such as meteorological conditions and plant transpiration, leading to significant delays in irrigation decisions. For example, an intelligent irrigation system introduced in a city park, lacking meteorological data, continued to execute a pre-set irrigation plan before a heavy rainstorm, resulting in water waste. Furthermore, the lack of coordination mechanisms between irrigation equipment in different areas prevents dynamic allocation based on overall water resource conditions. For instance, the irrigation needs of industrial and residential areas differ significantly, but existing systems struggle to achieve cross-regional optimized scheduling. In addition, the maintenance and management of traditional irrigation systems rely on manual inspections, resulting in long equipment failure response times, high pipeline leakage rates, and increased operation and maintenance costs and resource depletion.

[0004] Ecological environmental protection and urban landscape improvement place higher demands on greening irrigation. Traditional irrigation methods easily lead to soil salinization, with the area of ​​salinized soil in northern cities increasing, affecting plant growth. Simultaneously, the irrigation process lacks consideration for carbon emissions, resulting in a significant carbon footprint from electricity consumption. Furthermore, current technologies cannot quantify the impact of irrigation on ecosystem services such as biodiversity and air quality, making it difficult to meet the needs of urban ecological civilization construction. For example, improper irrigation strategies in some parks have led to mosquito breeding, triggering public complaints and reflecting the inadequacy of the existing system in balancing social and ecological benefits. Summary of the Invention

[0005] The present invention proposes a multi-regional collaborative management platform for irrigation of urban greening seedlings to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a multi-regional collaborative management platform for irrigation of urban greening seedlings, comprising:

[0007] Distributed environmental perception module: Deploys a composite sensor array and a drone-based collaborative monitoring network, employing multispectral imaging cameras combined with deep learning algorithms to identify water shortage characteristics; utilizes the normalized differential moisture index formula... Locating water-scarce areas, Green represents the reflectivity in the green light band, and NIR represents the reflectivity in the near-infrared band;

[0008] Plant growth model library: Construct a dynamic database, introduce a hybrid modeling method that combines machine learning with physical models; establish an environmental stress response mechanism, automatically select the corresponding modified model through decision tree algorithm, and iterate the model using federated learning technology;

[0009] Multi-objective optimization decision module: Constructs a four-dimensional optimization objective function based on the improved NSGA-III algorithm, and adopts a dynamic weight allocation strategy to adjust priorities; introduces a deep reinforcement learning framework, and dynamically adjusts the irrigation plan in combination with weather forecast data;

[0010] Intelligent irrigation execution system: The pipeline network design adopts a biomimetic leaf vein structure, integrates intelligent drip irrigation and sprinkler irrigation devices, and deploys an Internet of Things valve matrix; electromagnetic flow meters and pressure sensors are set up to monitor the pipeline network flow and pressure, and the variable frequency water pump speed is automatically adjusted through PID control algorithm;

[0011] The blockchain traceability management module constructs a dual-chain parallel architecture, using homomorphic encryption combined with zero-knowledge proof technology to protect privacy. It designs smart contracts to automatically execute irrigation service billing, using the formula C = ρ × V + τ × t, where C is the cost, ρ is the water price, V is the water consumption, τ is the equipment usage rate, and t is the equipment runtime. It also establishes an irrigation credit rating system to generate credit scores, using the formula S = μ1E. s +μ2C c +μ3P r S represents credit score, E represents credit score. s For water saving rate, C c P is the compliance coefficient. r For the timeliness of problem handling, μ1, μ2, and μ3 are weights;

[0012] Human-computer interaction decision-making module: Develop a metaverse visualization platform, integrate a natural language processing assistant, build a built-in digital sandbox function, establish a cross-regional experience sharing community, enable remote expert consultation, and develop a virtual reality training module.

[0013] Furthermore, the plant growth model library includes a disease and pest risk early warning sub-model, which, based on the random forest algorithm, integrates environmental factors and historical disease and pest data, and predicts the probability of disease occurrence through sliding window analysis technology; the carbon sequestration gain assessment module dynamically calculates carbon sequestration based on the plant growth model, combines the impact of irrigation strategies on plant growth, and uses the biomass expansion factor method to assess carbon sequestration changes; and the variety adaptability optimization unit simulates plant growth performance through genetic algorithms, considering survival rate, growth rate, and landscape effect indicators to screen the optimal introduction scheme.

[0014] Furthermore, the plant growth model library includes a disease and pest risk early warning sub-model, which, based on the random forest algorithm, integrates environmental factors and historical disease and pest data, and predicts the probability of disease occurrence through sliding window analysis technology; the carbon sequestration gain assessment module dynamically calculates carbon sequestration based on the plant growth model, combines the impact of irrigation strategies on plant growth, and uses the biomass expansion factor method to assess carbon sequestration changes; and the variety adaptability optimization unit simulates plant growth performance through genetic algorithms, considering survival rate, growth rate, and landscape effect indicators to screen the optimal introduction scheme.

[0015] Furthermore, the multi-objective optimization decision-making module includes an extreme weather prediction unit that generates virtual meteorological data and, in conjunction with historical extreme weather events, generates corresponding irrigation strategy suggestions through a conditional generative adversarial network; an economic cost dynamic assessment system that accesses real-time water price, electricity market price, and equipment depreciation rate data, and uses a dynamic programming algorithm to optimize irrigation schedules; and an ecological impact simulation module that uses numerical simulation methods to predict irrigation impacts based on the Richards equation and solute transport equation.

[0016] Furthermore, the intelligent irrigation execution system includes an adaptive fertigation device that integrates a 16-channel liquid flow control unit using microfluidic chip technology to mix 12 kinds of fertilizers; a rainwater-greywater-tap water intelligent switching system equipped with turbidity, pH, and residual chlorine sensors, which uses fuzzy control algorithms to determine water quality and prioritize the use of non-traditional water sources; and a pipeline health monitoring unit that uses acoustic emission sensors combined with wavelet packet analysis and convolutional neural networks to detect and locate pipeline leaks, assess the degree of leakage, and predict pipeline failure risks.

[0017] Furthermore, the blockchain traceability management module includes a cross-chain data interaction protocol that shares data from municipal water affairs, landscaping management, and environmental monitoring departments, and uses a combination of application layer protocols and blockchain smart contracts to reliably interact with data; the data storage encryption upgrade scheme uses homomorphic encryption combined with a hash tree structure to store irrigation data in blocks with encryption; and the irrigation equipment full life cycle management subsystem uses blockchain to record equipment procurement, installation, use, maintenance, and scrapping information, enabling transparent management and quality traceability.

[0018] Furthermore, the human-computer interaction decision-making module includes an emotional interactive interface that captures facial expressions and postures through a camera, analyzes and judges emotional states by combining voice tone, and intelligently adjusts the interface layout, prompt information color, and intensity; the expert collaborative diagnosis system collaborates with on-site personnel through remote desktop sharing and video conferencing functions; and the virtual reality training module constructs a multi-scenario training system, optimizing the fun and participation of training through gamification design and a points reward mechanism.

[0019] Furthermore, it also includes:

[0020] The urban green space ecological compensation accounting module assesses the impact of irrigation on ecosystem services based on the InVEST model combined with irrigation, plant growth, and environmental monitoring data. The irrigation equipment lifespan prediction unit uses an LSTM neural network combined with an attention mechanism to analyze equipment vibration, current, and temperature data, predict the remaining lifespan of the equipment, and formulate maintenance plans. The emergency irrigation dispatch system automatically switches to emergency mode in the event of urban water supply crises or natural disasters, prioritizing the survival of greenery in key areas and generating emergency irrigation plans to be reported to relevant departments.

[0021] Furthermore, it also includes:

[0022] Irrigation carbon emission accounting module: quantifies the carbon footprint of electricity consumption, equipment production and transportation, and water resource treatment, using a life cycle assessment method, and the calculation formula C. total =C elec +C equip +C water C total For total carbon emissions, C elec For carbon emissions from electricity, C equip For equipment carbon emissions, C water The system addresses carbon emissions from water resource management; the post-irrigation effect evaluation system calculates vegetation coverage, growth, and landscape satisfaction indicators through drone aerial surveys, ground quadrat surveys, and user feedback; the intelligent irrigation standard adaptation unit automatically matches irrigation quota standards based on urban greening level, plant type, and seasonal variation factors, and dynamically adjusts them according to local conditions.

[0023] Furthermore, it also includes:

[0024] Multilingual intelligent translation module: Based on neural machine translation technology, it translates languages ​​in real time. It adopts the Transformer architecture combined with the attention mechanism and uses natural language processing technology to extract knowledge from industry literature, academic papers and user cases. Through knowledge extraction, fusion and reasoning, it constructs an irrigation knowledge graph. Urban green space irrigation big data analysis platform integrates multi-source data and uses big data analysis technology to perform data mining to generate potential patterns and optimization strategies.

[0025] Compared with existing technologies, the beneficial effects of this invention are:

[0026] By leveraging multi-module collaborative innovation, the system achieves intelligent, precise, and efficient urban greening irrigation, significantly improving water resource utilization efficiency and ecological benefits. Utilizing distributed environmental sensing technology, the system deploys a composite sensor array and a drone-based collaborative monitoring network to achieve centimeter-level soil moisture monitoring and vegetation water shortage identification, resulting in a significantly faster irrigation decision-making response compared to traditional systems. In a city park application, the system has demonstrated significant water-saving effects, effectively alleviating urban water resource pressure.

[0027] Precision irrigation and scientific management have significantly improved plant growth. Based on dynamic water demand prediction using a plant growth model library and combined with a multi-objective optimization irrigation decision-making algorithm, vegetation cover, leaf greenness (SPAD value), and plant growth have all increased, while the incidence of pests and diseases has decreased. For example, in an ecological park application, irrigation strategies were optimized using a carbon sequestration assessment model to enhance the carbon sequestration capacity of urban green spaces.

[0028] The system has significantly improved both economic and social benefits. Through intelligent scheduling and equipment health monitoring, annual operating costs have decreased, with reductions in expenses such as water bills, electricity bills, equipment maintenance costs, and labor management fees. In a certain municipal greening project, the annual cost savings are considerable. Simultaneously, the virtual reality training module and the Metaverse collaborative platform shorten the training cycle for new employees, accelerate fault response, reduce public complaint rates, and improve landscape satisfaction. Furthermore, the system supports rainwater and greywater reuse, achieving high utilization rates of non-traditional water and increasing groundwater recharge, thus providing solid support for the sustainable development of the city's ecology. Attached Figure Description

[0029] Figure 1 This is a schematic block diagram of a multi-regional collaborative management platform for irrigation of urban greening seedlings proposed in this invention.

[0030] Figure 2 A diagram showing the comparison of water-saving effects in multiple regions;

[0031] Figure 3 A diagram showing the comparison of plant coverage indicators;

[0032] Figure 4 This is a diagram illustrating the comparison of economic benefits and costs.

[0033] Figure 5 A diagram showing the comparison of ecological benefit indicators;

[0034] Figure 6 A comparative diagram illustrating the improvement in social benefits. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention 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. Therefore, they should not be construed as limitations on this invention.

[0037] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0038] Reference Figures 1 to 6 A collaborative management platform for irrigation of urban greening seedlings in multiple regions, comprising:

[0039] Distributed environmental sensing module: Deploys a composite sensor array, including an ultrasonic anemometer (measurement accuracy ±0.05m / s, response time 0.2s), a four-component radiation sensor (total radiation, reflected radiation, and net radiation measurement error <3%), a soil water potential sensor (accuracy ±1.5kPa), and an EC value sensor (measurement range 0-10dS / m, accuracy ±2%). It employs a multispectral imaging camera (spectral resolution 5nm) combined with deep learning algorithms, based on an improved YOLOv8 model, to identify over 200 water shortage characteristics such as leaf curling and leaf color changes, achieving an accuracy of 99.5%. A collaborative monitoring network of fixed-wing and rotary-wing UAVs is configured. The fixed-wing UAVs are equipped with hyperspectral imagers (spectral range 400-2500nm) for large-area rapid scanning, while the rotary-wing UAVs are equipped with lidar (point cloud density 100 points / m²). 2 Three-dimensional modeling was performed on key areas, and the Normalized Difference Moisture Index (NDWI) was calculated using the formula. Where Green represents the reflectance in the green light band and NIR represents the reflectance in the near-infrared band, combined with a dynamic threshold segmentation algorithm, precise location of water-scarce areas can be achieved with a positioning error of <0.3m. 2 Simultaneously, an edge computing gateway (NVIDIA Jetson AGX Orin, with a computing power of 275 TOPS) was deployed, employing a pruned and optimized MobileNetV3 model to perform real-time noise reduction and feature extraction on sensor data, achieving a local data processing rate of 85%.

[0040] Plant Growth Model Library: A dynamic database containing over 180 varieties of urban greening seedlings has been constructed, integrating parameters for the entire life cycle, including seed germination, seedling growth, flowering, and fruiting. A hybrid modeling approach combining machine learning and physical models is introduced. For transpiration simulation, the Penman-Monteith formula combined with an LSTM neural network is used to correct parameters. For root growth simulation, a ROOTMAP model based on mechanical principles combined with reinforcement learning is used for dynamic adjustment. An environmental stress response mechanism is established. For stress conditions such as high temperature, low temperature, and drought, the corresponding correction model is automatically selected through a decision tree algorithm. For example, under drought stress, a correction coefficient based on soil moisture content is used to predict water demand using the formula W. d = a0 + a1T + a2H + a3L + a4SWC + , where W d Daily water demand (L / m³) 2 T represents the average daily temperature (°C), H represents the relative humidity (%), L represents the duration of sunlight (h), SWC represents the soil moisture content (%), and a0, a1, a2, a3, and a4 are model coefficients, serving as error terms to ensure a prediction error of <7%. The model supports online updates and utilizes federated learning technology to integrate historical data from different planting areas for model iteration while protecting data privacy.

[0041] A multi-objective optimization irrigation decision-making module is constructed based on an improved NSGA-III algorithm, combined with simulated annealing for local optimization. This module establishes a four-dimensional optimization objective function encompassing water-saving rate (target > 40%), plant health (stress index < 0.07), energy cost (reduction of 25%), and ecological protection (reduction of soil salinization risk by 30%). A dynamic weight allocation strategy is employed, adjusting objective priorities based on seasonal and regional characteristics, as well as municipal water policies. For example, the weight of plant health is increased during the high-temperature summer months, while the weight of water-saving rate is increased in water-scarce cities. A deep reinforcement learning framework is introduced, using irrigation strategies as actions and plant growth indicators and cost data as rewards. The strategy is iteratively optimized through historical irrigation data and plant growth feedback. For complex terrain areas, an improved ant colony algorithm is used for irrigation path planning, reducing head loss by 20% with a pipeline length increase of no more than 5%. By combining weather forecast data (accessing the China Meteorological Administration API, with a data update frequency of 15 minutes and an accuracy rate of 88%), and using a Long Short-Term Memory (LSTM) network to predict the weather conditions for the next 3 days, the irrigation plan is dynamically adjusted 48 hours in advance to achieve intelligent rain-sheltered irrigation and staggered peak irrigation.

[0042] Intelligent irrigation execution system: The pipeline design adopts a biomimetic leaf vein structure, optimizes pipe diameter based on Murray's law, and simulates water flow distribution under different working conditions through finite element analysis (ANSYS Fluent), reducing head loss along the pipeline by 22%. It integrates intelligent flexible drip irrigation tape driven by shape memory alloy, with dripper spacing automatically adjusting according to plant growth stages within a range of 10-60cm; it is equipped with a pressure pulsating sprinkler device, controlling the pulse frequency (20-60Hz) through a high-frequency solenoid valve (response time 10ms), combined with aerodynamically designed sprinklers, achieving an atomization particle size of 50-150μm and a water saving rate of 50%. An IoT valve matrix is ​​deployed, employing ZigBee 3.0 and NB-IoT dual-mode communication to achieve a minimum range of 30m. 2 The unit features independent and precise control, with a valve response time of <0.8 seconds and irrigation uniformity >93%. Electromagnetic flow meters (accuracy ±0.5%) and pressure sensors (accuracy ±0.005MPa) are installed to monitor pipeline flow and pressure in real time. A PID control algorithm automatically adjusts the speed of the variable frequency pump (efficiency >88%) to maintain stable pipeline pressure.

[0043] Blockchain Traceability and Management Module: A dual-chain parallel architecture is constructed. The main chain uses a consortium blockchain (Hyperledger Fabric) to store core irrigation data (water consumption, fertilizer usage records, irrigation time, etc.), while the side chain uses a private blockchain to store device operation and maintenance logs. Homomorphic encryption combined with zero-knowledge proof technology is employed to ensure data availability while protecting data privacy and supporting anonymous queries and verification. A smart contract is designed to automatically execute irrigation service billing, with the billing formula C = ρ × V + τ × t, where C is the cost (yuan) and ρ is the water price (yuan / m³). 3 V represents water consumption (m³) 3 τ represents the equipment usage rate (yuan / h), and t represents the equipment operating time (h). It also implements irrigation equipment maintenance reminders (triggered 30 days in advance), performance evaluation (based on indicators such as water saving rate and equipment failure rate), and dispute arbitration functions. An irrigation credit evaluation system is established, generating a credit score based on indicators such as user irrigation compliance and water-saving effectiveness. The scoring formula is S=μ1E s +μ2C c +μ3P r Where S is the credit score, E s For water saving rate, C c P is the compliance coefficient. r For the timeliness of problem handling, μ1, μ2, and μ3 are weights, and the credit score is linked to water price and service priority.

[0044] Human-Computer Interaction and Decision Support Module: Developing a metaverse visualization platform based on Unreal Engine 5 (UE5) to achieve a 1:1 high-precision digital twin scene, supporting VR / AR / MR multi-mode interaction. Integrating a natural language processing assistant based on BERT-Large fine-tuning, enabling semantic understanding and intelligent response to voice commands with a response time of <1.2 seconds. The platform includes a built-in digital sandbox function, utilizing Monte Carlo simulation combined with genetic algorithms to predict the effects of different irrigation strategies (accuracy >92%), and visually displaying the results through 3D animations and data charts. A cross-regional experience-sharing community is established, employing knowledge graph technology to integrate knowledge from industry literature and user cases, enabling intelligent knowledge retrieval and related recommendations. Support for real-time annotation of digital twin scenes by multiple parties enables remote expert consultation; a virtual reality training module is developed, constructing a highly realistic irrigation operation scenario, and shortening the new employee training cycle by 70% through task guidance and error feedback mechanisms.

[0045] In this invention, the distributed environmental sensing module also includes a self-organizing wireless sensor network with a mesh topology. Nodes are equipped with automatic power adjustment (adjustment range 1-100mW). When a node fails, the network reconstructs routes using an ant colony optimization algorithm, with a reconstruction time of <8 seconds. The solar-kinetic-mains power supply system uses high-efficiency cadmium telluride thin-film batteries (conversion efficiency 18%) for the solar panels and piezoelectric ceramic materials (energy conversion efficiency 15%) for the kinetic energy harvester. It is equipped with an intelligent switching circuit that prioritizes the use of renewable energy and automatically switches to mains power when energy is insufficient, ensuring stable operation of the sensors 24 / 7.

[0046] In this invention, the plant growth model library also includes a pest and disease risk early warning sub-model. Based on the random forest algorithm, it integrates 15 types of environmental factors such as temperature, humidity, rainfall, and wind speed with historical data of 40 pests and diseases. Through sliding window analysis technology, it predicts the probability of disease occurrence 96 hours in advance with an accuracy of 90%. The carbon sequestration gain assessment module dynamically calculates carbon sequestration based on the plant growth model and assesses carbon sequestration changes using the biomass expansion factor method, combined with the impact of irrigation strategies on plant growth. The variety adaptability optimization unit simulates plant growth performance under different irrigation strategies using a genetic algorithm. It comprehensively considers indicators such as survival rate, growth rate, and landscape effect to screen the optimal introduction scheme, thereby increasing the survival rate of introduced species by 35%.

[0047] In this invention, the multi-objective optimization decision-making module also includes an extreme weather prediction unit based on generative adversarial networks (GANs), which generates virtual meteorological data for the next 5 days and, combined with historical extreme weather events, generates corresponding irrigation strategy suggestions through conditional generative adversarial networks (cGANs); an economic cost dynamic evaluation system, which accesses real-time water price data (updated every hour), electricity market price data (updated every 15 minutes), and equipment depreciation rate data, and uses dynamic programming algorithms to optimize irrigation schedules, saving an average of 20% in costs annually; and an ecological impact simulation module, which uses numerical simulation methods based on the Richards equation and solute transport equation to predict the impact of irrigation on groundwater levels and soil salinity distribution, preventing secondary disasters such as soil salinization and groundwater pollution.

[0048] In this invention, the intelligent irrigation execution system also includes an adaptive water and fertilizer integration device, which adopts microfluidic chip technology and integrates a 16-channel liquid flow control unit (flow range 0.1-10L / h, accuracy ±0.3%) to achieve precise proportioning and mixing of 12 kinds of fertilizers; a rainwater-greywater-tap water intelligent switching system, equipped with a turbidity sensor (detection accuracy 0.05NTU), a pH sensor (accuracy ±0.05), and a residual chlorine sensor (accuracy ±0.01mg / L), which judges water quality through a fuzzy control algorithm and prioritizes the use of non-traditional water sources, achieving a non-traditional water utilization rate of 80%; and a pipeline health monitoring unit, which uses an acoustic emission sensor (sensitivity -70dB) combined with wavelet packet analysis and convolutional neural network to achieve pipeline leak detection, location (error <1.5m), and leak degree assessment, predicting pipeline failure risk 30 days in advance.

[0049] In this invention, the blockchain traceability and management module also includes a cross-chain data interaction protocol, which supports secure data sharing with municipal water systems, landscaping management departments, and environmental monitoring departments. It adopts an application layer protocol (such as CoAP) combined with blockchain smart contracts to achieve trusted data interaction. The data storage encryption upgrade scheme uses homomorphic encryption combined with a hash tree structure to encrypt and store irrigation data in blocks, ensuring data integrity while supporting encrypted retrieval and calculation. The irrigation equipment full life cycle management subsystem records information such as equipment procurement, installation, use, maintenance, and scrapping through blockchain, realizing transparent management and quality traceability of equipment.

[0050] In this invention, the human-computer interaction decision-making module also includes an emotional interaction interface. This interface captures the operator's facial expressions and postures via a camera, and combines this with voice tone analysis to determine the operator's emotional state. It then intelligently adjusts the interface layout and the color and intensity of prompts to enhance the user experience. An expert collaborative diagnosis system supports real-time annotation of digital twin scenarios by multiple parties. Through remote desktop sharing and video conferencing, it enables efficient collaboration between experts and on-site personnel. A virtual reality training module constructs a training system encompassing various scenarios such as equipment operation, troubleshooting, and irrigation strategy development. Through gamification and a points-based reward mechanism, it enhances the fun and engagement of the training.

[0051] This invention also includes the following modules:

[0052] The urban green space ecological compensation accounting module, based on the InVEST model and combined with irrigation data, plant growth data, and environmental monitoring data, assesses the impact of irrigation on biodiversity, air quality, soil and water conservation, and other ecological service functions, providing a basis for the formulation of urban ecological compensation policies. The irrigation equipment lifespan prediction unit uses an LSTM neural network combined with an attention mechanism to analyze multi-source data such as equipment vibration, current, and temperature to predict the remaining lifespan of the equipment with an accuracy of 88%, and to develop maintenance plans in advance. The emergency irrigation dispatch system automatically switches to emergency mode in emergencies such as urban water supply crises and natural disasters, prioritizing the survival of greenery in key areas (such as main urban roads and parks), while simultaneously generating emergency irrigation plans and reporting them to relevant departments.

[0053] This invention also includes the following modules:

[0054] Irrigation carbon emission accounting module: Quantifies the carbon footprint of electricity consumption, equipment production and transportation, water resource treatment, etc., using the Life Cycle Assessment (LCA) method, with the calculation formula being C. total =C elec +C equip +C water C total For total carbon emissions (kgCO2), C elec For carbon emissions from electricity, C equip For equipment carbon emissions, C water To address carbon emissions from water resource management, the system supports the generation of carbon footprint reports, contributing to green urban development. The post-irrigation effect evaluation system calculates indicators such as vegetation coverage, plant growth, and landscape satisfaction through a combination of UAV aerial surveys (0.1m resolution), ground quadrat surveys (200 quadrats / hectare), and user feedback, with an evaluation error of <4%, providing data support for optimizing irrigation strategies. The intelligent irrigation standard adaptation unit automatically matches irrigation quota standards based on factors such as urban greening level (e.g., parks, streets, residential areas), plant type, and seasonal changes, and dynamically adjusts them according to local conditions.

[0055] This invention also includes the following modules:

[0056] Multilingual Intelligent Translation Module: Based on neural machine translation technology, it supports real-time mutual translation between 10 languages ​​including Chinese, English, Japanese, Korean, French, and German. Employing a Transformer architecture combined with an attention mechanism, it achieves a translation accuracy rate of >96%, making it suitable for international collaborative projects. Irrigation Knowledge Graph Construction System: Utilizing natural language processing technology, it automatically extracts knowledge from industry literature, academic papers, and user cases. Through knowledge extraction, fusion, and reasoning, it constructs an irrigation knowledge graph containing concepts, relationships, and attributes, supporting intelligent question answering and knowledge recommendation. Urban Green Space Irrigation Big Data Analysis Platform: Integrating multi-source data such as meteorological data, geographic information data, economic data, and historical irrigation data, it uses big data analytics technologies (such as Hadoop and Spark) for data mining to discover potential patterns and optimization strategies in irrigation management, providing a scientific basis for urban greening decisions.

[0057] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A multi-regional collaborative management platform for irrigation of urban greening seedlings, characterized in that, Includes the following modules: Distributed environmental perception module: Deploys a composite sensor array and a drone-based collaborative monitoring network, and uses a multispectral imaging camera combined with deep learning algorithms to identify water shortage characteristics; The normalized differential moisture index formula was used. Locating water-scarce areas, Green represents the reflectivity in the green light band, and NIR represents the reflectivity in the near-infrared band; Plant growth model library: Construct a dynamic database, introduce a hybrid modeling method that combines machine learning with physical models; establish an environmental stress response mechanism, automatically select the corresponding modified model through decision tree algorithm, and iterate the model using federated learning technology; Multi-objective optimization decision module: Constructs a four-dimensional optimization objective function based on the improved NSGA-III algorithm, and adopts a dynamic weight allocation strategy to adjust priorities; introduces a deep reinforcement learning framework, and dynamically adjusts the irrigation plan in combination with weather forecast data; Intelligent irrigation execution system: The pipeline network design adopts a biomimetic leaf vein structure, integrates intelligent drip irrigation and sprinkler irrigation devices, and deploys an Internet of Things valve matrix; electromagnetic flow meters and pressure sensors are set up to monitor the pipeline network flow and pressure, and the variable frequency water pump speed is automatically adjusted through PID control algorithm; The blockchain traceability management module constructs a dual-chain parallel architecture, using homomorphic encryption combined with zero-knowledge proof technology to protect privacy. It designs smart contracts to automatically execute irrigation service billing, using the formula C = ρ × V + τ × t, where C is the cost, ρ is the water price, V is the water consumption, τ is the equipment usage rate, and t is the equipment runtime. It also establishes an irrigation credit rating system to generate credit scores, using the formula S = μ1E. s +μ2C c +μ3P r S represents credit score, E represents credit score. s For water saving rate, C c P is the compliance coefficient. r For the timeliness of problem handling, μ1, μ2, and μ3 are weights; Human-computer interaction decision-making module: Develop a metaverse visualization platform, integrate a natural language processing assistant, build a built-in digital sandbox function, establish a cross-regional experience sharing community, enable remote expert consultation, and develop a virtual reality training module.

2. The multi-regional greening seedling irrigation collaborative management platform for urban greening seedling planting according to claim 1, characterized in that, The distributed environmental sensing module includes a self-organizing wireless sensor network with a mesh topology. Nodes are equipped with automatic power adjustment. When a node fails, the network reconstructs routes through an ant colony optimization algorithm. A solar-kinetic-mains power supply system is constructed. The solar panels use cadmium telluride thin-film batteries, and the kinetic energy collector uses piezoelectric ceramic materials. It is equipped with an intelligent switching circuit that prioritizes the use of renewable energy and automatically switches to mains power when energy is insufficient.

3. The multi-regional greening seedling irrigation collaborative management platform for urban greening seedling planting according to claim 1, characterized in that, The plant growth model library includes a disease and pest risk early warning sub-model, which is based on the random forest algorithm, integrates environmental factors and historical disease and pest data, and predicts the probability of disease occurrence through sliding window analysis technology; the carbon sequestration gain assessment module dynamically calculates carbon sequestration based on the plant growth model, combines the impact of irrigation strategies on plant growth, and uses the biomass expansion factor method to assess carbon sequestration changes; the variety adaptability optimization unit simulates plant growth performance through genetic algorithms, considers survival rate, growth rate, and landscape effect indicators, and selects the optimal introduction scheme.

4. The multi-regional greening seedling irrigation collaborative management platform for urban greening seedling planting according to claim 1, characterized in that, The multi-objective optimization decision-making module includes an extreme weather prediction unit that generates virtual meteorological data and combines it with historical extreme weather events to generate corresponding irrigation strategy suggestions through a conditional generative adversarial network; an economic cost dynamic assessment system that accesses real-time water price, electricity market price, and equipment depreciation rate data and uses a dynamic programming algorithm to optimize irrigation schedules; and an ecological impact simulation module that uses numerical simulation methods based on the Richards equation and solute transport equation to predict irrigation impacts.

5. The multi-regional greening seedling irrigation collaborative management platform for urban greening seedling planting according to claim 1, characterized in that, The intelligent irrigation execution system includes an adaptive fertigation device that integrates a 16-channel liquid flow control unit using microfluidic chip technology to mix 12 kinds of fertilizers; a rainwater-greywater-tap water intelligent switching system equipped with turbidity, pH, and residual chlorine sensors, which uses fuzzy control algorithms to determine water quality and prioritize the use of non-traditional water sources; and a pipeline health monitoring unit that uses acoustic emission sensors combined with wavelet packet analysis and convolutional neural networks to detect and locate pipeline leaks, assess the degree of leakage, and predict pipeline failure risks.

6. The multi-regional greening seedling irrigation collaborative management platform for urban greening seedling planting according to claim 1, characterized in that, The blockchain traceability management module includes a cross-chain data interaction protocol that shares data from municipal water affairs, landscaping management, and environmental monitoring departments. It uses a combination of application layer protocols and blockchain smart contracts to exchange data reliably. The data storage encryption upgrade scheme uses homomorphic encryption combined with a hash tree structure to store irrigation data in blocks with encryption. The irrigation equipment full life cycle management subsystem uses blockchain to record equipment procurement, installation, use, maintenance, and scrapping information, enabling transparent management and quality traceability.

7. The multi-regional greening seedling irrigation collaborative management platform for urban greening seedling planting according to claim 1, characterized in that, The human-computer interaction decision-making module includes an emotional interactive interface that captures facial expressions and postures through a camera, analyzes and judges emotional states by combining voice tone, and intelligently adjusts the interface layout, prompt information color, and intensity; the expert collaborative diagnosis system collaborates with on-site personnel through remote desktop sharing and video conferencing functions; and the virtual reality training module constructs a multi-scenario training system, optimizing the fun and participation of training through gamification design and a points reward mechanism.

8. The multi-regional greening seedling irrigation collaborative management platform for urban greening seedling planting according to claim 1, characterized in that, Also includes: The urban green space ecological compensation accounting module assesses the impact of irrigation on ecosystem services based on the InVEST model combined with irrigation, plant growth, and environmental monitoring data. The irrigation equipment lifespan prediction unit uses an LSTM neural network combined with an attention mechanism to analyze equipment vibration, current, and temperature data, predict the remaining lifespan of the equipment, and formulate maintenance plans. The emergency irrigation dispatch system automatically switches to emergency mode in the event of urban water supply crises or natural disasters, prioritizing the survival of greenery in key areas and generating emergency irrigation plans to be reported to relevant departments.

9. The multi-regional greening seedling irrigation collaborative management platform for urban greening seedling planting according to claim 1, characterized in that, Also includes: Irrigation carbon emission accounting module: quantifies the carbon footprint of electricity consumption, equipment production and transportation, and water resource treatment, using a life cycle assessment method, and the calculation formula C. total =C elec +C equip +C water C total For total carbon emissions, C elec For carbon emissions from electricity, C equip For equipment carbon emissions, C water The system addresses carbon emissions from water resource management; the post-irrigation effect evaluation system calculates vegetation coverage, growth, and landscape satisfaction indicators through drone aerial surveys, ground quadrat surveys, and user feedback; the intelligent irrigation standard adaptation unit automatically matches irrigation quota standards based on urban greening level, plant type, and seasonal variation factors, and dynamically adjusts them according to local conditions.

10. The multi-regional greening seedling irrigation collaborative management platform for urban greening seedling planting according to claim 1, characterized in that, Also includes: Multilingual intelligent translation module: Based on neural machine translation technology, it translates languages ​​in real time. It adopts the Transformer architecture combined with the attention mechanism and uses natural language processing technology to extract knowledge from industry literature, academic papers and user cases. Through knowledge extraction, fusion and reasoning, it constructs an irrigation knowledge graph. Urban green space irrigation big data analysis platform integrates multi-source data and uses big data analysis technology to perform data mining to generate potential patterns and optimization strategies.

Images