Urban greening intelligent irrigation control method and device

Abstract

The invention discloses an urban landscaping intelligent irrigation control method and device, and relates to the technical field of intelligent control, and the method comprises the steps: obtaining urban landscaping distribution information, carrying out the irrigation demand analysis, and determining a plurality of subdivided irrigation regions through clustering; for each subdivided irrigation area, generating a plurality of original irrigation schemes in combination with the growth cycle of vegetation; on the basis of Internet of Things sensing modules deployed in all regions, environment state and vegetation growth state data are collected in real time, regional weather prediction information is combined, an original irrigation scheme is dynamically optimized, a personalized irrigation scheme is generated, and intelligent irrigation control is carried out accordingly. The technical problems that in the prior art, a traditional irrigation mode lacks precise irrigation control over vegetation in different areas, irrigation cost is high, and diversification requirements are difficult to effectively meet are solved, and the technical effects that the irrigation cost and water resource waste are reduced through regional precise irrigation, and the diversification requirements of vegetation in different areas are met are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an urban greening intelligent irrigation control method and device. Background Art

[0002] In urban greening management, irrigation is an important part of maintaining healthy vegetation growth. However, traditional irrigation methods mostly rely on manual experience and lack precise control over the regional environment, vegetation growth status, and water resource utilization, which often leads to low irrigation efficiency and serious waste of water resources. In addition, due to the vast area of ​​urban greening areas and the wide variety of vegetation, the irrigation requirements of various types of vegetation in different growth cycles also vary greatly, so traditional irrigation methods are difficult to effectively meet diverse needs. Summary of the invention

[0003] The present application provides an intelligent irrigation control method and device for urban greening, which is used to solve the technical problems in the prior art that traditional irrigation methods lack precise irrigation control of vegetation in different areas, have high irrigation costs and are difficult to effectively meet diverse needs.

[0004] In a first aspect of the present application, a method for intelligent irrigation control of urban greening is provided, the method comprising: obtaining urban greening distribution information, the urban greening distribution information comprising vegetation types and basic vegetation information of each region; performing irrigation demand analysis according to the urban greening distribution information, and clustering the demand according to the demand analysis results to determine a plurality of segmented irrigation areas; generating a plurality of corresponding original irrigation schemes for the plurality of segmented irrigation areas in combination with the vegetation growth cycle law; deploying Internet of Things sensor modules respectively based on the plurality of segmented irrigation areas to collect the real-time environmental status and real-time vegetation growth status of each segmented area in real time; dynamically optimizing the plurality of original irrigation schemes according to the real-time environmental status and real-time vegetation growth status in combination with regional meteorological forecast data to generate a plurality of personalized irrigation schemes, and performing intelligent irrigation control according to the personalized irrigation schemes.

[0005] According to a second aspect of the present application, there is provided an intelligent irrigation control device for urban greening, the device comprising: an urban greening distribution information acquisition module, the urban greening distribution information acquisition module is used to acquire urban greening distribution information, the urban greening distribution information includes vegetation types and basic vegetation information of each region; an irrigation demand analysis module, the irrigation demand analysis module is used to perform irrigation demand analysis according to the urban greening distribution information, and cluster the demand according to the demand analysis result to determine a plurality of subdivided irrigation areas; an original irrigation scheme generation module, the original irrigation scheme generation module is used to generate a plurality of corresponding original irrigation schemes for the plurality of subdivided irrigation areas in combination with the vegetation growth cycle law; a real-time status acquisition module, the real-time status acquisition module is used to deploy Internet of Things sensor modules respectively based on the plurality of subdivided irrigation areas, and collect the real-time environmental status and real-time vegetation growth status of each subdivided area in real time; an intelligent irrigation control module, the intelligent irrigation control module is used to dynamically optimize the plurality of original irrigation schemes according to the real-time environmental status and real-time vegetation growth status in combination with regional meteorological forecast data, generate a plurality of personalized irrigation schemes, and perform intelligent irrigation control according to the personalized irrigation schemes.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The present application provides an urban greening intelligent irrigation control method and device, which relate to the field of intelligent control technology. By acquiring urban greening distribution information, irrigation demand analysis and clustering are performed to determine multiple subdivided irrigation areas, an original irrigation plan is generated according to the vegetation growth cycle, and an Internet of Things sensor module is deployed to collect the environment and vegetation growth status in real time. Combined with regional meteorological forecast data, the original plan is dynamically optimized to generate a personalized irrigation plan, and intelligent irrigation control is performed accordingly. The technical problems in the prior art that traditional irrigation methods lack precise irrigation control of vegetation in different regions, have high irrigation costs and are difficult to effectively meet diverse needs are solved, and the technical effect of reducing irrigation costs and water resource waste through regional precise irrigation and meeting the diverse needs of vegetation in different regions is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of a flow chart of an urban greening intelligent irrigation control method provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of the structure of an intelligent irrigation control device for urban greening provided in an embodiment of the present application.

[0011] Explanation of reference numerals: urban greening distribution information acquisition module 11, irrigation demand analysis module 12, original irrigation plan generation module 13, real-time status acquisition module 14, intelligent irrigation control module 15. DETAILED DESCRIPTION

[0012] The present application provides an intelligent irrigation control method and device for urban greening, which is used to solve the technical problems in the prior art that traditional irrigation methods lack precise irrigation control of vegetation in different areas, have high irrigation costs and are difficult to effectively meet diverse needs.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0015] Embodiment 1, as Figure 1 As shown, the present application provides an urban greening intelligent irrigation control method, the method comprising:

[0016] P10: Obtain urban greening distribution information, which includes vegetation types and basic vegetation information in each area.

[0017] First, obtain the urban greening distribution information, including the vegetation type and basic vegetation information of each area, that is, the greening layout of different areas in the city, including key data such as plant species, planting area, and vegetation density. This information is the basic data source of the intelligent irrigation system and directly affects the subsequent irrigation demand analysis and plan generation.

[0018] Specifically, the vegetation distribution data of each area can be automatically obtained through the combination of geographic information system (GIS) and urban planning database. Vegetation type refers to the type of plants planted in each area, such as lawns, shrubs, trees, etc. Different types of vegetation have different growth characteristics and irrigation requirements. The basic vegetation information includes the growth years, planting area and water demand of specific plants in each area at each growth stage. For example, trees have less water demand in the initial growth period, but as the trees grow, the water demand will gradually increase. Therefore, collecting this basic information can provide an accurate basis for subsequent irrigation demand analysis.

[0019] By integrating with remote sensing technology or drone data collection, urban greening distribution information can be efficiently updated to ensure that the acquired data is accurate and timely. In addition, IoT devices can be deployed in various greening areas to collect environmental data such as local climate and soil moisture in real time, providing data support for a comprehensive understanding of vegetation status. The greening distribution information obtained in this way not only includes the macroscopic urban greening distribution, but also the microscopic vegetation characteristics and their growth needs, providing an accurate data basis for subsequent intelligent irrigation.

[0020] P20: According to the urban greening distribution information, an irrigation demand analysis is performed, and demand clustering is performed according to the demand analysis results to determine a plurality of subdivided irrigation areas.

[0021] Furthermore, step P20 of the embodiment of the present application further includes:

[0022] P21: Based on the urban greening distribution information, extract the vegetation type and basic vegetation information of each region, and the basic vegetation information includes but is not limited to vegetation type, planting area, and growth habit; P22: According to the vegetation type and basic vegetation information of each region, perform irrigation demand analysis according to different vegetation types, and obtain multiple irrigation demand sequences of multiple vegetation types, and the multiple irrigation demand sequences contain irrigation demand information of multiple vegetation types at each growth node; P23: Perform multi-order demand clustering based on the multiple irrigation demand sequences to determine multiple subdivided irrigation areas.

[0023] It should be understood that based on the urban greening distribution information, irrigation demand analysis is performed and multiple subdivided irrigation areas are determined through clustering algorithms. This step provides a basis for precise control of intelligent irrigation by classifying and analyzing the growth requirements of vegetation in each area.

[0024] First, the data extraction technology is used to accurately screen the vegetation types and basic vegetation information of each region from the urban greening distribution information database. This information is the core of the irrigation demand analysis, including but not limited to the specific types of vegetation, the size of the planting area, and their unique growth habits, such as drought tolerance and water demand. Through these data, we can initially understand the basic irrigation needs of vegetation in different regions.

[0025] Next, an irrigation demand analysis is performed based on the basic information of each vegetation type. Irrigation demand analysis refers to determining the irrigation demand at different stages based on the growth cycle, water demand and climatic conditions of each vegetation. In this process, the different growth nodes of different types of vegetation in the entire growth cycle (such as germination period, growth period, maturity period, fruiting period, etc.) are considered, and the irrigation demand sequences of different vegetation types are generated according to the specific needs of each growth node. Each sequence records in detail the key information such as the irrigation amount, irrigation frequency and irrigation time of the vegetation type at each growth node, providing a scientific basis for the subsequent formulation of irrigation plans.

[0026] Finally, the demand clustering algorithm is introduced to perform multi-order demand clustering on the generated multiple irrigation demand sequences, that is, multiple clustering is performed according to different clustering targets. Demand clustering can use a variety of clustering algorithms, such as K-means, hierarchical clustering, etc. This algorithm can identify vegetation areas with similar irrigation needs and cluster them into multiple subdivided irrigation areas. The vegetation in each subdivided area has a high degree of consistency in irrigation demand, which is conducive to the formulation of more accurate and efficient irrigation plans for each area. Through multi-order demand clustering, not only the level of refinement of irrigation management is improved, but also the optimal allocation of water resources can be achieved, providing a basis for the subsequent formulation of precise irrigation plans.

[0027] Furthermore, step P23 of the embodiment of the present application also includes:

[0028] P23-1: Based on the multiple irrigation demand sequences, extract multiple irrigation cycle sequences of multiple vegetation types, and the multiple irrigation cycle sequences include multiple irrigation time nodes; P23-2: Based on the multiple irrigation cycle sequences, perform first-order demand clustering on the multiple vegetation types to obtain first-order demand clustering results; P23-3: Based on the multiple irrigation demand sequences, extract multiple irrigation water demand sequences of multiple vegetation types, and the multiple irrigation water demand sequences include water demand of multiple vegetation types at different irrigation time nodes; P23-4: Based on the multiple irrigation water demand sequences, perform second-order demand clustering on the first-order demand clustering results to obtain second-order demand clustering results; P23-5: With reference to the second-order demand clustering results, perform synchronous irrigation area division on the multiple vegetation types to determine multiple subdivided irrigation areas, wherein each subdivided irrigation area includes multiple local irrigation areas with the same irrigation demand.

[0029] Optionally, the specific process of performing multi-order demand clustering may be, first, based on multiple irrigation demand sequences, extracting irrigation cycle sequences of multiple vegetation types, which contain time nodes when each vegetation needs to be irrigated at different growth stages. These time nodes define the irrigation frequency of the vegetation, for example, a lawn may need daily irrigation, while a tree may need weekly irrigation. By extracting these cycle sequences, the irrigation timing of different vegetation can be more clearly understood, providing a basis for subsequent clustering.

[0030] Furthermore, based on the above multiple irrigation cycle sequences, multiple vegetation types were preliminarily clustered. At this stage, the basis for clustering was the similarity of demand of different vegetation at different irrigation time nodes. Different vegetation were grouped according to the characteristics of similar irrigation cycles to form the first-order demand clustering results. This clustering result helped us preliminarily divide the similarity areas in irrigation time.

[0031] After completing the initial clustering, we further extracted the irrigation water demand sequences of each type of vegetation at different irrigation time nodes. These sequences record the water demand of vegetation at each irrigation time node. For example, lawns may require a lot of water during the rapid growth period, while shrubs may require less water. Through these water demand sequences, more accurate irrigation calculations can be provided for each type of vegetation.

[0032] Based on multiple irrigation water demand sequences, the first-order clustering results are further refined to group each type of vegetation more accurately, which is the second-order demand clustering. At this time, clustering not only considers the similarity of irrigation cycles, but also comprehensively considers water demand. Through second-order demand clustering, vegetation with similar irrigation cycles and water demand can be grouped into one group to form a more targeted irrigation area.

[0033] Finally, referring to the results of the second-order demand clustering, multiple vegetation types are divided into synchronous irrigation areas to determine multiple subdivided irrigation areas. The vegetation in these areas has a high degree of consistency in irrigation time and irrigation amount, which can facilitate the formulation of personalized irrigation plans for each subdivided area. At the same time, each subdivided irrigation area also contains multiple local irrigation areas with the same irrigation needs. These local areas can be managed as a whole during irrigation, thereby improving the efficiency and accuracy of irrigation management.

[0034] Furthermore, the embodiment of the present application further includes step P23-5a, and step P23-5a further includes:

[0035] P23-51a: Obtain specific planting location information of multiple vegetation types, and obtain regional soil type information based on the specific planting location information; P23-52a: Based on the regional soil type information, correct the second-order demand clustering results, and perform synchronous irrigation area division based on the corrected second-order demand clustering results to determine multiple subdivided irrigation areas.

[0036] In a possible embodiment of the present application, in order to further optimize the division of irrigation areas and ensure that the irrigation plan not only considers the vegetation type and its needs, but also considers the impact of soil type on irrigation effect. Specifically, first, by obtaining the specific planting location information of each vegetation type, its exact location in the urban greening area is determined. Through these location information, the system can associate and obtain the corresponding regional soil type information. Soil type refers to the properties of the soil in the area, such as soil permeability, water content, water retention capacity, etc. These factors will directly affect the effect of irrigation. For example, sandy soil has strong permeability and water is easy to lose, so the amount and frequency of irrigation may need to be increased, while clay soil has relatively good water retention, and the frequency of irrigation can be reduced.

[0037] Based on the soil type information obtained, the system modifies the second-order demand clustering results generated previously. The basis for the modification is the water retention capacity and permeability of different soil types. For example, under the same vegetation type, if the water retention capacity of the soil in a certain area is poor, the irrigation frequency or amount of the area needs to be increased. Therefore, the modified clustering results will be more accurate and fully consider the balance between the irrigation demand of vegetation and soil characteristics.

[0038] Through this correction process, multiple subdivided irrigation areas can be finally divided to ensure that the irrigation strategy of each area not only meets the growth needs of vegetation, but also adapts to its soil conditions, forming a more synchronized irrigation strategy. This method can significantly improve the scientific nature of irrigation and resource utilization efficiency, reduce water waste, and ensure that vegetation under different soil conditions can receive the most suitable irrigation.

[0039] P30: For the multiple subdivided irrigation areas, combined with the vegetation growth cycle law, generate corresponding multiple original irrigation plans.

[0040] Specifically, for the multiple subdivided irrigation areas divided in the early stage, combined with the growth cycle of various types of vegetation, multiple corresponding original irrigation plans are generated. The growth cycle law refers to the changes in demand shown by vegetation at different stages of its growth process, usually including the germination period, rapid growth period, maturity period and dormancy period. At each stage, the demand for resources such as water and nutrition of vegetation will be different.

[0041] First, we conducted an in-depth study of the growth cycle of vegetation in each subdivided area. This included understanding the key growth stages of different vegetation types, such as germination, growth, flowering, fruiting, and dormancy, as well as the specific needs of each stage for environmental factors such as water, nutrients, and light. We constructed a detailed vegetation growth cycle model through literature research, expert consultation, and historical data analysis.

[0042] Next, based on these growth cycle models and combined with the specific environmental conditions of each subdivided area (such as soil type, rainfall, evaporation, etc.), irrigation design software is used to make a preliminary design of the irrigation plan. In this process, it is necessary to fully consider the changes in vegetation water demand at different growth stages and the impact of irrigation methods (such as drip irrigation, sprinkler irrigation, micro-sprinkler irrigation, etc.) on water use efficiency. By simulating the dynamic changes in soil moisture under different irrigation schemes, the irrigation effects of each scheme are evaluated, and a preliminary irrigation scheme that can both meet the growth needs of vegetation and effectively save water resources is selected.

[0043] Optionally, in order to ensure the feasibility and effectiveness of the irrigation scheme, field verification and adjustment can also be carried out. In the selected subdivision area, an irrigation test field is set up, and actual irrigation operations are carried out according to the initially designed irrigation scheme, and the growth status of vegetation and soil moisture conditions are monitored in real time. By comparing the vegetation growth performance and water resource utilization efficiency under different irrigation schemes, the original irrigation scheme can be further optimized and improved.

[0044] P40: Based on the multiple subdivided irrigation areas, IoT sensor modules are deployed respectively to collect the real-time environmental status and real-time vegetation growth status of each subdivided area in real time.

[0045] Furthermore, step P40 of the embodiment of the present application also includes:

[0046] P41: Based on the multiple subdivided irrigation areas, the area information of each area is obtained respectively, and according to the area information, IoT sensor modules of different sizes are deployed; P42: The IoT sensor module includes corresponding multi-sensor device components, and each IoT sensor module is associated with an edge processing node; P43: According to the multi-sensor device components, the real-time environmental data and vegetation growth characteristics of each subdivided area are collected and transmitted to the associated edge processing node to evaluate the vegetation growth environment and growth status, and obtain the real-time environmental status and real-time vegetation growth status of each subdivided area.

[0047] It should be understood that precise deployment of IoT sensor modules is implemented for each subdivided irrigation area to achieve comprehensive monitoring of the real-time environmental status and vegetation growth status of the area.

[0048] First, according to the area information of each subdivided irrigation area, scientifically plan and deploy IoT sensor modules of different sizes. This step needs to fully consider the relationship between the area and the number and distribution density of sensors to ensure the comprehensiveness and accuracy of the monitoring data. Through precise calculations, different numbers and configurations of IoT sensor modules are deployed for areas of different sizes. Larger irrigation areas may require more sensor equipment to ensure data collection coverage, while smaller areas only require fewer devices.

[0049] The IoT sensor module includes multiple types of sensor devices, i.e., multi-sensor device components, such as temperature sensors, humidity sensors, light sensors, soil moisture sensors, etc. These sensors can fully reflect the actual growth status of vegetation by collecting environmental and growth data in the area. At the same time, each IoT sensor module is associated with an edge processing node for real-time processing of the collected data. The edge processing node is a computing node close to the data source, which can perform preliminary processing and analysis of the data locally, reduce data transmission delays, and improve data processing efficiency.

[0050] Through the collaborative work of the multi-sensing device components, the IoT sensor module can collect environmental data such as temperature, humidity, light intensity, soil moisture, and vegetation growth characteristics (such as chlorophyll content, stem thickness, plant height, etc.) in each subdivided area in real time. These data are then transmitted to the associated edge processing node for further analysis and evaluation. The edge processing node can use the built-in algorithm model to quickly process the received data to evaluate the current vegetation growth environment and growth status. Finally, the real-time environmental status and real-time vegetation growth status of each subdivided area are obtained, providing strong data support for the dynamic adjustment and optimization of subsequent irrigation plans.

[0051] P50: Based on the real-time environmental status and the real-time vegetation growth status, the multiple original irrigation plans are dynamically optimized in combination with regional meteorological forecast data, multiple personalized irrigation plans are generated, and intelligent irrigation control is performed according to the personalized irrigation plans.

[0052] Furthermore, step P50 of the embodiment of the present application also includes:

[0053] P51: Based on the multiple original irrigation plans, randomly extract the first original irrigation plan, and match the first real-time environmental state and the first vegetation growth state based on the real-time environmental state and the vegetation growth state; P52: According to the first real-time environmental state and the first vegetation growth state, compare the first expected environmental state and the first expected vegetation growth state to generate a first environmental compensation value and a first growth state compensation value; P53: With irrigation time, irrigation frequency, irrigation duration and water consumption as optimization variables, and the first environmental compensation value and the first growth state compensation value as optimization targets, the first original irrigation plan is regulated and optimized to generate multiple alternative irrigation plans; P54: With irrigation cost, water resource utilization rate and expected vegetation growth state as optimization targets, the multiple alternative irrigation plans are optimized to obtain a first personalized irrigation plan; P55: And so on, traverse the multiple original irrigation plans to generate multiple personalized irrigation plans.

[0054] Optionally, based on the real-time environmental status and real-time vegetation growth status of each subdivided irrigation area, combined with regional meteorological forecast data, multiple original irrigation plans generated previously are dynamically optimized to generate multiple personalized irrigation plans, and intelligent irrigation control is implemented according to these personalized plans. Ensure that the vegetation in each area can obtain the most appropriate irrigation amount and irrigation time under different environmental conditions, thereby optimizing water resource utilization and vegetation growth effects.

[0055] First, one is randomly selected from multiple original irrigation schemes as a starting point, namely, the first original irrigation scheme, and according to the current real-time collected environmental state and vegetation growth state, the first real-time environmental state and the first vegetation growth state of the corresponding subdivided area that match it are found.

[0056] Then, in the P52 stage, by comparing the first real-time environmental state and the first expected environmental state, as well as the difference between the first vegetation growth state and the first expected vegetation growth state, a first environmental compensation value and a first growth state compensation value are generated. The first environmental compensation value and the first growth state compensation value represent the difference between the current environment or vegetation growth state and the original expectation, and are used to adjust the irrigation strategy, which can provide a clear direction for subsequent optimization adjustments. Among them, the first expected environmental state and the first expected vegetation growth state are the ideal environmental state and the ideal growth state of each growth stage pre-set according to the vegetation growth characteristics.

[0057] Furthermore, the first original irrigation scheme is regulated and optimized with irrigation time, irrigation frequency, irrigation duration and water consumption as optimization variables, and the first environmental compensation value and the first growth state compensation value as optimization targets. Multiple alternative irrigation schemes are generated through complex optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.). In this process, the system adjusts the original irrigation scheme by adjusting the irrigation duration and water consumption according to the actual situation. For example, under hot and dry conditions, the system may increase the irrigation frequency or extend the irrigation duration to compensate for the current environmental changes.

[0058] Subsequently, multiple alternative irrigation schemes are further optimized. With irrigation cost, water resource utilization rate and expected vegetation growth status as comprehensive optimization targets, multi-objective optimization technology (such as NSGA-II algorithm) is used to comprehensively evaluate and sort these schemes. By balancing irrigation cost and vegetation growth requirements, the scheme with the best comprehensive performance is selected as the first personalized irrigation scheme. This scheme can not only meet the real-time growth needs of vegetation, but also save water resources as much as possible and reduce irrigation costs.

[0059] Finally, all the original irrigation plans are traversed according to the above process, and corresponding personalized irrigation plans are generated for each subdivided irrigation area. These plans fully consider the real-time environmental conditions and vegetation growth status differences in different areas, and realize the personalization and refinement of irrigation management.

[0060] Furthermore, step P54 of the embodiment of the present application also includes:

[0061] P54-1: Based on the overall irrigation demand, configure optimization weights for the irrigation cost, water resource utilization rate and expected vegetation growth status; P54-2: Combined with the optimization weights, compare and analyze the multiple alternative irrigation plans, and extract the alternative irrigation plan with the best fitness as the first personalized irrigation plan.

[0062] Specifically, the weight configuration and fitness analysis of the optimized irrigation scheme can be introduced to ensure that the irrigation scheme finally selected can achieve the optimal effect based on the balance of multiple objectives.

[0063] First, based on the overall irrigation demand, optimization weights are configured for the three optimization goals of irrigation cost, water resource utilization rate, and expected vegetation growth status. The optimization weights are configured to balance the relationship between the three optimization goals. For example, when water resources are relatively scarce, a higher weight can be given to water resource utilization rate, while when pursuing rapid vegetation growth, the weight of expected vegetation growth status can be increased. Through this weight configuration, the system can flexibly adjust the priority of the optimization goals to ensure that the solution that best meets the current needs is selected.

[0064] In terms of technical implementation, the configuration of optimization weights can be based on a variety of factors, such as real-time meteorological conditions, regional water resource tension, vegetation health, etc. These weights can be dynamically adjusted through a multi-objective decision-making model to adapt to different irrigation needs and environmental changes.

[0065] After the weights are configured, all alternative irrigation schemes will be compared and analyzed based on these weights. The fitness of each scheme is calculated by combining the performance of different irrigation schemes in terms of irrigation cost, water resource utilization rate and vegetation growth status. Fitness is an indicator used to evaluate the comprehensive performance of a scheme in multi-objective optimization. The alternative irrigation scheme with the best fitness can be extracted as the final first personalized irrigation scheme. For example, an alternative irrigation scheme may perform well in water resource utilization rate, but the irrigation cost is slightly higher, while another scheme may have an advantage in irrigation cost, but the water resource utilization rate is slightly worse. Through the comparative analysis of comprehensive optimization weights, the system can select the most balanced scheme to ensure the healthy growth of vegetation while saving costs and efficiently using water resources.

[0066] Among them, the fitness calculation can use the weighted comprehensive scoring model or the fitness function in the genetic algorithm, and finally select the optimal solution by assigning weights to each optimization target. The execution of this step can greatly improve the accuracy and effectiveness of the irrigation plan, ensure that the intelligent irrigation system can still maintain efficient operation in a dynamic environment, and achieve optimal resource allocation and vegetation management.

[0067] In summary, the embodiments of the present application have at least the following technical effects:

[0068] This application obtains urban greening distribution information (including vegetation type and basic information), analyzes and clusters irrigation needs, and determines multiple subdivided irrigation areas. Generates original irrigation plans based on vegetation growth cycles, deploys IoT sensor modules to collect real-time environmental and vegetation growth status, and dynamically optimizes the original plans in combination with regional meteorological forecast data to generate personalized irrigation plans, and performs intelligent irrigation control accordingly.

[0069] The technical effect of reducing irrigation costs and water waste through regional precision irrigation and meeting the diverse needs of vegetation in different regions has been achieved.

[0070] Embodiment 2, based on the same inventive concept as the urban greening intelligent irrigation control method in the above embodiment, Figure 2 As shown, the present application provides an intelligent irrigation control device for urban greening, and the device and method embodiments in the present application are based on the same inventive concept. The device includes:

[0071] The urban greening distribution information acquisition module 11 is used to acquire urban greening distribution information, and the urban greening distribution information includes vegetation types and basic vegetation information of each region.

[0072] The irrigation demand analysis module 12 is used to perform irrigation demand analysis according to the urban greening distribution information, and to cluster the demand according to the demand analysis results to determine a plurality of subdivided irrigation areas.

[0073] The original irrigation scheme generating module 13 is used to generate a plurality of corresponding original irrigation schemes for the plurality of subdivided irrigation areas in combination with the vegetation growth cycle law.

[0074] The real-time status acquisition module 14 is used to deploy IoT sensor modules based on the multiple subdivided irrigation areas, respectively, to collect the real-time environmental status and real-time vegetation growth status of each subdivided area in real time.

[0075] The intelligent irrigation control module 15 is used to dynamically optimize the multiple original irrigation plans according to the real-time environmental status and the real-time vegetation growth status in combination with regional meteorological forecast data, generate multiple personalized irrigation plans, and perform intelligent irrigation control according to the personalized irrigation plans.

[0076] Furthermore, the irrigation demand analysis module 12 is further configured to perform the following steps:

[0077] Based on the urban greening distribution information, the vegetation type and basic vegetation information of each region are extracted, and the basic vegetation information includes but is not limited to vegetation type, planting area, and growth habit; according to the vegetation type and basic vegetation information of each region, irrigation demand analysis is performed according to different vegetation types to obtain multiple irrigation demand sequences of multiple vegetation types, and the multiple irrigation demand sequences include irrigation demand information of multiple vegetation types at each growth node; based on the multiple irrigation demand sequences, multi-order demand clustering is performed to determine multiple subdivided irrigation areas.

[0078] Furthermore, the irrigation demand analysis module 12 is further configured to perform the following steps:

[0079] According to the multiple irrigation demand sequences, multiple irrigation cycle sequences of multiple vegetation types are extracted, and the multiple irrigation cycle sequences include multiple irrigation time nodes; based on the multiple irrigation cycle sequences, the multiple vegetation types are first-order demand clustering is performed to obtain first-order demand clustering results; according to the multiple irrigation demand sequences, multiple irrigation water demand sequences are extracted, and the multiple irrigation water demand sequences include water demand of multiple vegetation types at different irrigation time nodes; based on the multiple irrigation water demand sequences, the first-order demand clustering results are second-order demand clustering is performed to obtain second-order demand clustering results; with reference to the second-order demand clustering results, the multiple vegetation types are synchronously irrigated and multiple subdivided irrigation areas are determined, wherein each subdivided irrigation area includes multiple local irrigation areas with the same irrigation demand.

[0080] Furthermore, the irrigation demand analysis module 12 is further configured to perform the following steps:

[0081] Specific planting location information of multiple vegetation types is obtained, and regional soil type information is obtained based on the specific planting location information; based on the regional soil type information, the second-order demand clustering result is corrected, and the synchronous irrigation area is divided according to the corrected second-order demand clustering result to determine multiple subdivided irrigation areas.

[0082] Furthermore, the real-time status acquisition module 14 is also used to perform the following steps:

[0083] Based on the multiple subdivided irrigation areas, the area information of each area is obtained respectively, and according to the area information, IoT sensor modules of different sizes are deployed; the IoT sensor module includes a corresponding multi-sensor device component, and each IoT sensor module is associated with an edge processing node; according to the multi-sensor device component, the real-time environmental data and vegetation growth characteristics of each subdivided area are collected and transmitted to the associated edge processing node to evaluate the vegetation growth environment and growth status, and obtain the real-time environmental status and real-time vegetation growth status of each subdivided area.

[0084] Furthermore, the intelligent irrigation control module 15 is also used to perform the following steps:

[0085] Based on the multiple original irrigation schemes, a first original irrigation scheme is randomly selected, and based on the real-time environmental state and the vegetation growth state, the first real-time environmental state and the first vegetation growth state are matched; according to the first real-time environmental state and the first vegetation growth state, the first expected environmental state and the first expected vegetation growth state are compared to generate a first environmental compensation value and a first growth state compensation value; with irrigation time, irrigation frequency, irrigation duration and water consumption as optimization variables, the first environmental compensation value and the first growth state compensation value as optimization targets, the first original irrigation scheme is regulated and optimized to generate multiple alternative irrigation schemes; with irrigation cost, water resource utilization rate and expected vegetation growth state as optimization targets, the multiple alternative irrigation schemes are optimized to obtain a first personalized irrigation scheme; and by analogy, the multiple original irrigation schemes are traversed to generate multiple personalized irrigation schemes.

[0086] Furthermore, the intelligent irrigation control module 15 is also used to perform the following steps:

[0087] Based on the overall irrigation demand, optimization weights are configured for the irrigation cost, water resource utilization rate and expected vegetation growth status; combined with the optimization weights, the multiple alternative irrigation schemes are compared and analyzed to extract the alternative irrigation scheme with the best fitness as the first personalized irrigation scheme.

[0088] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0089] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0090] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for controlling urban greening intelligent irrigation, characterized in that: The method comprises: Acquire urban greening distribution information, wherein the urban greening distribution information includes vegetation types and basic vegetation information in each area; According to the urban greening distribution information, an irrigation demand analysis is performed, and demand clustering is performed according to the demand analysis results to determine a plurality of subdivided irrigation areas; For the plurality of subdivided irrigation areas, generating a plurality of corresponding original irrigation plans in combination with the vegetation growth cycle law; Based on the multiple subdivided irrigation areas, IoT sensor modules are deployed respectively to collect the real-time environmental status and real-time vegetation growth status of each subdivided area in real time; According to the real-time environmental status and the real-time vegetation growth status, the multiple original irrigation plans are dynamically optimized in combination with regional meteorological forecast data, multiple personalized irrigation plans are generated, and intelligent irrigation control is performed according to the personalized irrigation plans.

2. The urban greening intelligent irrigation control method according to claim 1, characterized in that: According to the urban greening distribution information, an irrigation demand analysis is conducted, and demand clustering is performed based on the demand analysis results to determine multiple subdivided irrigation areas, including: Extracting vegetation types and basic vegetation information of each region based on the urban greening distribution information, wherein the basic vegetation information includes but is not limited to vegetation type, planting area, and growth habit; According to the vegetation types and basic vegetation information of each region, irrigation demand analysis is performed according to different vegetation types to obtain multiple irrigation demand sequences of multiple vegetation types, wherein the multiple irrigation demand sequences include irrigation demand information of multiple vegetation types at each growth node; Multi-order demand clustering is performed based on the multiple irrigation demand sequences to determine multiple subdivided irrigation areas.

3. The urban greening intelligent irrigation control method according to claim 2, characterized in that: Performing multi-order demand clustering based on the multiple irrigation demand sequences to determine multiple subdivided irrigation areas includes: Extracting a plurality of irrigation cycle sequences of a plurality of vegetation types according to the plurality of irrigation demand sequences, wherein the plurality of irrigation cycle sequences include a plurality of irrigation time nodes; Based on the multiple irrigation cycle sequences, performing first-order demand clustering on the multiple vegetation types to obtain a first-order demand clustering result; Extracting a plurality of irrigation water demand sequences of a plurality of vegetation types according to the plurality of irrigation demand sequences, wherein the plurality of irrigation water demand sequences include water demand of the plurality of vegetation types at different irrigation time nodes; Based on the multiple irrigation water demand sequences, performing second-order demand clustering on the first-order demand clustering results to obtain second-order demand clustering results; Referring to the second-order demand clustering result, the multiple vegetation types are divided into synchronous irrigation areas to determine a plurality of subdivided irrigation areas, wherein each subdivided irrigation area includes a plurality of local irrigation areas with the same irrigation demand.

4. The urban greening intelligent irrigation control method according to claim 1, characterized in that: Based on the multiple subdivided irrigation areas, IoT sensor modules are deployed respectively to collect the real-time environmental status and real-time vegetation growth status of each subdivided area in real time, including: Based on the multiple subdivided irrigation areas, area information of each area is obtained, and IoT sensor modules of different sizes are deployed according to the area information of the area; The IoT sensor module includes corresponding multi-sensor device components, and each IoT sensor module is associated with an edge processing node; According to the multi-sensor equipment component, real-time environmental data and vegetation growth characteristics of each subdivided area are collected and transmitted to the associated edge processing node to evaluate the vegetation growth environment and growth status, thereby obtaining the real-time environmental status and real-time vegetation growth status of each subdivided area.

5. The urban greening intelligent irrigation control method according to claim 1, characterized in that: According to the real-time environmental status and vegetation growth status, the multiple original irrigation plans are dynamically optimized in combination with regional meteorological forecast data to generate multiple personalized irrigation plans, including: Based on the multiple original irrigation schemes, randomly extract a first original irrigation scheme, and based on the real-time environmental state and the vegetation growth state, match the first real-time environmental state and the first vegetation growth state; According to the first real-time environmental state and the first vegetation growth state, a first expected environmental state and a first expected vegetation growth state are compared to generate a first environmental compensation value and a first growth state compensation value; With irrigation time, irrigation frequency, irrigation duration and water consumption as optimization variables, and the first environmental compensation value and the first growth state compensation value as optimization targets, the first original irrigation plan is regulated and optimized to generate multiple alternative irrigation plans; Taking irrigation cost, water resource utilization rate and expected vegetation growth status as optimization targets, optimizing the multiple alternative irrigation schemes to obtain a first personalized irrigation scheme; In this way, the multiple original irrigation plans are traversed to generate multiple personalized irrigation plans.

6. The urban greening intelligent irrigation control method according to claim 5, characterized in that: Taking irrigation cost, water resource utilization rate and expected vegetation growth status as optimization targets, the plurality of alternative irrigation schemes are optimized to obtain a first personalized irrigation scheme, including: Based on the overall irrigation demand, optimizing weights are configured for the irrigation cost, water resource utilization rate and expected vegetation growth state; In combination with the optimization weight, the multiple alternative irrigation schemes are compared and analyzed, and the alternative irrigation scheme with the best fitness is extracted as the first personalized irrigation scheme.

7. The urban greening intelligent irrigation control method according to claim 3, characterized in that: The method further comprises: Acquire specific planting location information of multiple vegetation types, and acquire regional soil type information in association with the specific planting location information; Based on the regional soil type information, the second-order demand clustering result is corrected, and the synchronous irrigation area is divided according to the corrected second-order demand clustering result to determine a plurality of subdivided irrigation areas.

8. An intelligent irrigation control device for urban greening, characterized in that: The device comprises: An urban greening distribution information acquisition module, which is used to acquire urban greening distribution information, wherein the urban greening distribution information includes vegetation types and basic vegetation information of each region; An irrigation demand analysis module, wherein the irrigation demand analysis module is used to perform irrigation demand analysis according to the urban greening distribution information, and to cluster the demand according to the demand analysis results to determine a plurality of subdivided irrigation areas; An original irrigation scheme generating module, wherein the original irrigation scheme generating module is used to generate a plurality of corresponding original irrigation schemes for the plurality of subdivided irrigation areas in combination with the vegetation growth cycle law; A real-time status acquisition module, wherein the real-time status acquisition module is used to deploy IoT sensor modules respectively based on the multiple subdivided irrigation areas, and collect the real-time environmental status and real-time vegetation growth status of each subdivided area in real time; An intelligent irrigation control module is used to dynamically optimize the multiple original irrigation plans according to the real-time environmental status and the real-time vegetation growth status in combination with regional meteorological forecast data, generate multiple personalized irrigation plans, and perform intelligent irrigation control according to the personalized irrigation plans.

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