A data-driven intelligent green roof construction and maintenance method and system

Abstract

This application relates to the technical field of roof greening, and in particular to a data-driven intelligent roof greening construction and maintenance method and system. This application first determines overall maintenance parameters based on greening scheme selection information, then divides functional areas into zones according to roof area characteristics and sets differentiated maintenance adjustment coefficients, performing basic maintenance through intelligent maintenance instructions. Simultaneously, a real-time monitoring mechanism for zone growth conditions is established. When an anomaly is detected, an early warning is immediately triggered, environmental data is collected, and maintenance plans are dynamically adjusted. This achieves intelligent maintenance based on real-time data, improving the survival rate and growth quality of roof greening.

Description

Technical Field

[0001] The present application relates to the technical field of roof greening, and in particular to a data-driven intelligent roof greening construction and maintenance method and system. Background Art

[0002] With the acceleration of urbanization, the urban heat island effect and air pollution are becoming increasingly prominent. As a sustainable form of urban greening, rooftop greening not only improves the urban ecological environment but also enhances building energy efficiency, playing an important role in urban ecological construction.

[0003] Currently, roof greening maintenance mainly relies on automatic sprinkler systems and preset maintenance procedures for management. Environmental data is collected through sensor networks, and basic maintenance measures such as irrigation and ventilation are automatically adjusted based on preset thresholds.

[0004] However, the existing roof greening maintenance parameters are fixed and difficult to adapt to the dynamic changes in plant growth. They are unable to provide timely warnings and intelligent adjustments for abnormal situations, resulting in unsatisfactory maintenance effects. This situation needs further improvement. Summary of the Invention

[0005] To address the problem that existing green roof maintenance parameters are fixed and difficult to adapt to the dynamic changes in plant growth, this application provides a data-driven intelligent green roof construction and maintenance method and system, which adopts the following technical solutions:

[0006] In a first aspect, the present application provides a data-driven intelligent green roof construction and maintenance method, comprising the following steps:

[0007] Obtaining greening scheme selection information, and obtaining overall maintenance parameter information based on the greening scheme selection information;

[0008] Obtaining roof area characteristic information, dividing the roof into several areas according to the roof area characteristic information, and obtaining the area maintenance adjustment coefficient;

[0009] triggering intelligent maintenance instructions according to the partition maintenance adjustment coefficient and the overall maintenance parameter information;

[0010] Acquire partition growth status detection information, compare the partition growth status detection information with preset standard growth parameters to obtain a growth difference value, compare the growth difference value with a preset change threshold value, and if the growth difference value is outside the preset change threshold range, trigger an abnormality warning and data collection instruction;

[0011] Obtaining partition adjustment parameter information according to the abnormal warning and data collection instructions;

[0012] According to the partition adjustment parameter information, a maintenance plan adjustment instruction is triggered.

[0013] By adopting the above technical solution, the existing roof greening maintenance parameters are fixed, and the sun area and shade area of ​​the same roof may use the same irrigation frequency, resulting in uneven plant growth conditions, or it may be impossible to timely discover and adjust the maintenance plan when the plants show early signs of disease. This application first determines the overall maintenance parameters based on the greening plan selection information, and then performs functional zoning according to the characteristics of the roof area and sets differentiated maintenance adjustment coefficients, and performs basic maintenance through intelligent maintenance instructions; at the same time, a real-time monitoring mechanism for the growth status of the zoning is established. When an abnormality is detected, an early warning is immediately triggered and environmental data is collected to dynamically adjust the maintenance plan; intelligent maintenance based on real-time data is realized, and the survival rate and growth quality of roof greening are improved.

[0014] Optionally, the greening scheme selection information includes plant species, greening area, and desired coverage, and the overall maintenance parameter information includes a suitable temperature range, humidity range, lighting requirements, and maintenance cycle. Acquiring the greening scheme selection information and acquiring the overall maintenance parameter information based on the greening scheme selection information specifically includes the following steps:

[0015] Extracting plant variety information from the greening scheme selection information, and obtaining corresponding suitable temperature range, humidity range and light requirements according to the plant variety database;

[0016] Obtaining a maintenance cycle based on the greening area and desired coverage, combined with the growth characteristics of the plant species;

[0017] The suitable temperature range, humidity range, light requirements and curing period are correlated and integrated to obtain the overall curing parameter information.

[0018] By adopting the above technical scheme, in order to solve the one-sided problems existing in the maintenance parameter setting process of the existing roof greening system, such as setting fixed irrigation parameters only according to the plant variety and ignoring the influence of the greening area and the expected coverage, resulting in uneven growth of ground cover plants such as clover when planted on a large scale due to improper setting of maintenance parameters; this application first extracts plant variety information from the greening plan and queries the professional database to obtain basic growth parameters, and then calculates the maintenance cycle with the greening area and the expected coverage as influencing factors. Finally, a complete overall maintenance parameter system is established through correlation and integration, and a maintenance parameter determination method considering multi-dimensional factors is established, thereby improving the scientificity and applicability of the maintenance plan.

[0019] Optionally, the maintenance cycle includes a growth period maintenance cycle and a dormancy period maintenance cycle. The maintenance cycle is obtained based on the greening area and the desired coverage, combined with the growth characteristics of the plant variety, and specifically includes the following steps:

[0020] According to the growth characteristics and expected coverage of the plant variety, the growing period maintenance cycle and the dormant period maintenance cycle are obtained, wherein the growing period maintenance cycle includes the growing period irrigation frequency, the growing period fertilization frequency, and the growing period pruning frequency, and the dormant period maintenance cycle includes the dormant period irrigation frequency, the dormant period fertilization frequency, and the dormant period pruning frequency;

[0021] An annual maintenance plan schedule is determined based on the growing period maintenance cycle and the dormant period maintenance cycle.

[0022] By adopting the above technical solution, in order to solve the problem that the existing roof greening maintenance plan fails to fully consider the cyclical characteristics of plant growth, this application first determines the specific frequency of various maintenance operations during the growth period and dormancy period based on the growth characteristics and expected coverage of the plant variety, and then integrates these differentiated parameters into the annual maintenance plan schedule to achieve periodic adjustment of maintenance measures, thereby improving maintenance efficiency and resource utilization.

[0023] Optionally, the roof area characteristic information includes orientation, sunshine duration, and shading conditions; obtaining the roof area characteristic information, dividing the roof into several areas according to the roof area characteristic information, and obtaining the area maintenance adjustment coefficient specifically includes the following steps:

[0024] Divide the roof into several areas according to the sunshine duration and the orientation, and obtain the illumination intensity coefficient of each area;

[0025] According to the shading condition and the partition illumination intensity coefficient, a partition maintenance adjustment coefficient is determined, and the partition maintenance adjustment coefficient is used to perform regional adjustment on the overall maintenance parameter information.

[0026] By adopting the above technical solution, in the same roof space, there are significant differences in lighting conditions between the unobstructed area facing south and the area facing north that is blocked by adjacent buildings. If a unified maintenance parameter is used, it will lead to insufficient water supply in the area with sufficient light and water accumulation in the area with insufficient light. This application first comprehensively considers the two key factors of orientation and sunshine duration to divide the roof into regions and quantify the light intensity coefficient. Then, the obstruction situation is used as a correction parameter combined with the light intensity coefficient to establish a zoned maintenance adjustment coefficient, thereby realizing regional adjustment of the overall maintenance parameters and improving the growth adaptability and maintenance effect of plants in different regions.

[0027] Optionally, obtaining partition adjustment parameter information according to the abnormal warning and data collection instructions specifically includes the following steps:

[0028] Acquire the zoned environmental monitoring data and zoned plant growth status data according to the abnormal warning and data collection instructions;

[0029] Obtaining an abnormality cause analysis based on the sub-area environmental monitoring data and the sub-area plant growth status data in combination with the growth difference value;

[0030] According to the abnormal cause analysis and the overall maintenance parameter information, the type of maintenance parameter to be adjusted and the adjustment range are determined, and the partition adjustment parameter information is generated.

[0031] By adopting the above technical solution, this application first synchronously collects environmental monitoring data and plant growth status data after the abnormal warning is triggered, and then correlates and analyzes the real-time data with the growth difference value to identify the cause of the abnormality. Finally, based on the comparison of the abnormal cause and the overall maintenance parameters, the type of parameters that need to be adjusted and the specific adjustment range are determined, thereby improving the efficiency of solving abnormal problems and the optimization ability of maintenance plans.

[0032] Optionally, triggering a maintenance plan adjustment instruction based on the partition adjustment parameter information specifically includes the following steps:

[0033] Extracting a maintenance adjustment type from the partition adjustment parameter information, where the maintenance adjustment type includes irrigation adjustment, fertilization adjustment, and pruning adjustment;

[0034] For irrigation adjustments, the adjustment values ​​of irrigation water volume and irrigation duration are calculated based on soil moisture monitoring data;

[0035] For fertilization adjustments, determine the adjusted values ​​of fertilizer ratio and fertilizer amount based on soil nutrient monitoring data;

[0036] For pruning adjustments, the adjustment values ​​of pruning height and pruning range are determined based on plant growth density data;

[0037] Compare each adjustment value with the plant tolerance range, generate a corresponding execution instruction sequence, and trigger the maintenance plan adjustment instruction.

[0038] By adopting the above technical solutions, the existing roof greening maintenance adjustment solutions are too simplistic. For example, when adjusting the irrigation amount, the water consumption is increased or decreased based on experience, without considering the actual soil moisture content and plant tolerance, which leads to new problems after the adjustment and even causes plant damage. This application first subdivides the maintenance adjustment types into three aspects: irrigation, fertilization and pruning, and then introduces corresponding special monitoring data as the basis for adjustment, such as soil moisture content, nutrient content and growth density, and calculates each adjustment value based on this. Finally, the safety of the adjustment plan is ensured by comparing it with the plant tolerance range, thereby improving the accuracy and safety of the maintenance adjustment and reducing the probability of secondary damage caused by improper adjustment.

[0039] In a second aspect, the present application provides a data-driven intelligent green roof construction and maintenance system, comprising:

[0040] A greening scheme selection module is used to obtain greening scheme selection information and obtain overall maintenance parameter information based on the greening scheme selection information;

[0041] A zone division module is used to obtain roof zone characteristic information, divide the roof into several functional zones according to the roof zone characteristic information, and obtain zone maintenance adjustment coefficients;

[0042] An intelligent maintenance control module, configured to trigger intelligent maintenance instructions based on the partition maintenance adjustment coefficient and the overall maintenance parameter information;

[0043] A growth status monitoring module is used to obtain partition growth status detection information, compare the partition growth status detection information with preset standard growth parameters to obtain a growth difference value, compare the growth difference value with a preset change threshold value, and trigger an abnormal warning and data collection instruction if the growth difference value is outside the preset change threshold range;

[0044] A parameter adjustment module, configured to obtain partition adjustment parameter information according to the abnormal warning and data collection instructions;

[0045] The maintenance execution module is used to trigger a maintenance plan adjustment instruction according to the partition adjustment parameter information.

[0046] Optionally, the greening scheme selection module includes:

[0047] a parameter acquisition unit, configured to extract plant variety information from the greening scheme selection information, wherein the greening scheme selection information includes plant variety, greening area, and expected coverage;

[0048] A database query unit, used to obtain the corresponding suitable temperature range, humidity range and light requirements according to the plant variety database;

[0049] a maintenance cycle calculation unit, configured to obtain a maintenance cycle based on the greening area and the desired coverage, in combination with the growth characteristics of the plant species;

[0050] The parameter integration unit is used to associate and integrate the suitable temperature range, humidity range, light requirements and maintenance cycle to obtain the overall maintenance parameter information, wherein the overall maintenance parameter information includes the suitable temperature range, humidity range, light requirements and maintenance cycle.

[0051] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned data-driven intelligent roof greening construction and maintenance method are implemented.

[0052] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned data-driven intelligent roof greening construction and maintenance method.

[0053] In summary, this application includes at least one of the following beneficial technical effects:

[0054] 1. This application first determines overall maintenance parameters based on greening plan selection information. It then divides rooftop functional areas into zones based on their characteristics and sets differentiated maintenance adjustment coefficients. Basic maintenance is then performed through intelligent maintenance instructions. A real-time monitoring mechanism for zoned growth is also established. When anomalies are detected, an alert is immediately triggered, environmental data is collected, and maintenance plans are dynamically adjusted. This achieves intelligent maintenance based on real-time data, improving the survival rate and growth quality of rooftop greening.

[0055] 2. To address the one-sided issues in the maintenance parameter setting process of existing rooftop greening systems, such as setting fixed irrigation parameters based solely on plant species while ignoring the impact of greening area and desired coverage, which can lead to uneven growth of ground cover plants like clover when planted over large areas due to improper maintenance parameter settings, this application first extracts plant species information from the greening plan and queries a professional database to obtain basic growth parameters. It then uses greening area and desired coverage as influencing factors to calculate the maintenance cycle. Finally, through correlation and integration, a complete overall maintenance parameter system is established, establishing a maintenance parameter determination method that considers multiple factors, thereby improving the scientific nature and applicability of the maintenance plan.

[0056] 3. In order to address the problem that existing roof greening maintenance plans fail to fully consider the cyclical characteristics of plant growth, this application first determines the specific frequency of various maintenance operations during the growing and dormant periods based on the growth characteristics and expected coverage of the plant varieties, and then integrates these differentiated parameters into the annual maintenance plan schedule to achieve periodic adjustment of maintenance measures, thereby improving maintenance efficiency and resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of a data-driven intelligent green roof construction and maintenance method according to an embodiment of the present application;

[0058] Figure 2 This is a flow chart of step S100 in a data-driven intelligent green roof construction and maintenance method according to an embodiment of the present application;

[0059] Figure 3 This is a flow chart of step S120 in a data-driven intelligent green roof construction and maintenance method according to an embodiment of the present application;

[0060] Figure 4 This is a flow chart of step S200 in a data-driven intelligent green roof construction and maintenance method according to an embodiment of the present application;

[0061] Figure 5 This is a flow chart of step S500 in a data-driven intelligent green roof construction and maintenance method according to an embodiment of the present application;

[0062] Figure 6 This is a flow chart of step S600 in a data-driven intelligent green roof construction and maintenance method according to an embodiment of the present application;

[0063] Figure 7 This is a module diagram of a data-driven intelligent green roof construction and maintenance system according to an embodiment of the present application;

[0064] Figure 8 This is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0065] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0066] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0067] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0068] In the first aspect, the present application provides a data-driven intelligent roof greening construction and maintenance method, referring to Figure 1 , including the following steps:

[0069] S100: Obtain greening scheme selection information, and obtain overall maintenance parameter information based on the greening scheme selection information.

[0070] In this embodiment, the intelligent management platform collects greening project planning information provided by the construction unit through its interactive interface. The system analyzes this input using an expert knowledge base and, based on factors such as the project location's climate and architectural characteristics, generates preliminary greening plan recommendations and corresponding maintenance standard parameter sets.

[0071] Specifically, a commercial rooftop garden project planned to create a 1,000-square-meter leisure green space, primarily featuring low-maintenance, drought-tolerant plants. The system recommended a plant composition of 50% Creeping Grass, 30% Sedum, and 20% Sedum, and generated corresponding maintenance standards: irrigation every two days at a watering rate of 1.5 L / ㎡, and fertilization every 45 days at a rate of 15 g / ㎡.

[0072] S200: Obtain roof area characteristic information, divide the roof into several areas according to the roof area characteristic information, and obtain the area maintenance adjustment coefficient.

[0073] In this example, drones equipped with thermal imaging and multispectral sensors scan the rooftop environment, acquiring environmental data such as thermal distribution, light intensity, and moisture distribution. Based on this data, the system identifies areas with similar environmental characteristics and calculates corresponding maintenance adjustment factors based on the degree of deviation in each area's environmental characteristics.

[0074] For example, in an 800-square-meter green roof project, drones scanned the environment at different times, and the system identified five characteristic areas: the east sunlit area, the west shaded area, the central transition area, the north high wind area, and the south heat island area. The system then calculated a maintenance adjustment factor for each area: first, the environmental reference baseline value was determined, and the percentage deviation between the measured data for each area and the baseline value was used as the adjustment factor. For example, the east sunlit area had an average light intensity of 4400 lux (with a deviation of +10%), a temperature of 27°C (with a deviation of +8%), a relative humidity of 60% (with a deviation of -8%), and a wind speed of 2.8 m / s (with a deviation of -7%). This comprehensive calculation resulted in an adjustment factor of 0.9.

[0075] S300: triggering intelligent maintenance instructions based on the partition maintenance adjustment coefficient and overall maintenance parameter information.

[0076] In this embodiment, by learning historical maintenance data and plant growth feedback, a correlation model of environmental factors, maintenance measures, and growth effects is established. According to the adjustment coefficients of different regions, maintenance parameters are automatically optimized and accurate maintenance instructions are generated.

[0077] Specifically, for heat island areas, the system found based on machine learning model analysis that the area needed additional cooling measures, so it generated differentiated maintenance instructions: increase spray cooling, adjust irrigation time (avoid the high temperature period at noon), and recommend the addition of sunshade facilities.

[0078] S400. Obtain partition growth status detection information, compare the partition growth status detection information with preset standard growth parameters to obtain a growth difference value, compare the growth difference value with a preset change threshold value, and if the growth difference value is outside the preset change threshold range, trigger an abnormal warning and data collection instruction.

[0079] In this example, a computer vision-based plant growth monitoring system was pre-deployed, consisting of fixed cameras and patrol robots. The system uses image processing technology to analyze plant growth conditions in real time, including leaf color, growth density, and plant height, and intelligently compares them with standard growth parameters in a database. In the absence of hardware, manual patrol records are used to obtain growth status information for each area.

[0080] For example, the system detected an average plant height of 8cm in a high-wind zone, a -33% difference from the standard 12cm. This exceeded the preset ±20% variation threshold. The system immediately initiated a deep monitoring program, deploying patrol robots to scan the area at close range, collecting detailed data on soil compaction and root growth.

[0081] S500: Obtain partition adjustment parameter information according to the abnormal warning and data collection instructions.

[0082] In this embodiment, a plant growth expert knowledge base and an environmental stress response strategy library are pre-integrated. By performing multi-dimensional analysis on abnormal data, the root cause of the problem can be quickly identified and a scientific adjustment plan can be formulated.

[0083] For example, by analyzing abnormal data in strong wind areas, the system discovered: excessive soil compaction, which restricted root growth; and wind speeds frequently exceeding 6 m / s, which caused mechanical damage to plants. Based on this information, it generated comprehensive adjustment recommendations, including soil improvement and the installation of smart wind barriers.

[0084] S600: Trigger a maintenance plan adjustment instruction based on the partition adjustment parameter information.

[0085] In this embodiment, the adjustment plan is converted into specific equipment control instructions and manual operation guidelines through automated maintenance equipment and an intelligent management platform, and remote control and real-time supervision are achieved through Internet of Things technology.

[0086] In one embodiment, the greening scheme selection information includes plant species, greening area and desired coverage, and the overall maintenance parameter information includes suitable temperature range, humidity range, light requirements and maintenance cycle; Figure 2In step S100, greening scheme selection information is obtained, and overall maintenance parameter information is obtained based on the greening scheme selection information, which specifically includes the following steps:

[0087] S110 , extracting plant variety information from the greening plan selection information, and obtaining corresponding suitable temperature range, humidity range, and light requirements according to a plant variety database.

[0088] In this embodiment, the system pre-builds a multi-dimensional plant variety database. This database not only contains basic plant physiological characteristics but also integrates actual planting data and growth performance across different climate regions. Using machine learning algorithms, the system can quickly match optimal growth parameter ranges based on plant variety information, improving the accuracy and adaptability of parameter acquisition.

[0089] S120. Obtain a maintenance cycle based on the greening area and desired coverage, combined with the growth characteristics of the plant species.

[0090] In this embodiment, the system calculates the maintenance cycle and key time nodes required to achieve the expected effect by analyzing the growth rate, tillering ability and coverage expansion characteristics of the plants, combining the greening area and expected coverage requirements.

[0091] Specifically, for a project with a total area of ​​200 square meters and a desired coverage of 90%, the system analyzed the characteristics of each grass species in the mixed seeding plan: bermudagrass had a growth rate of 0.8 cm / day and a tillering cycle of 12 days, bermudagrass had a growth rate of 0.7 cm / day and a tillering cycle of 14 days, and ryegrass had a growth rate of 1.0 cm / day and a tillering cycle of 10 days. Growth simulations determined that basic coverage would be achieved in 45 days and the desired coverage would be achieved in 90 days. Based on this, a three-phase maintenance cycle was developed: an early phase (e.g., 1-30 days) focused on promoting germination and growth, a mid-phase focused on strengthening tillering expansion, and a late phase focused on optimizing cover density.

[0092] S130: Correlate and integrate the suitable temperature range, humidity range, lighting requirements, and curing cycle to obtain overall curing parameter information.

[0093] In this embodiment, the optimal overall maintenance parameter combination is generated by analyzing the mutual influence relationship between various growth parameters.

[0094] In one embodiment, the maintenance cycle includes a growth period maintenance cycle and a dormancy period maintenance cycle, referring to Figure 3 In step S120, the maintenance cycle is obtained based on the greening area and the desired coverage, combined with the growth characteristics of the plant species, which specifically includes the following steps:

[0095] S121. Obtain a growing period maintenance cycle and a dormant period maintenance cycle based on the growth characteristics and desired coverage of the plant variety.

[0096] Among them, the maintenance cycle during the growing period includes the irrigation frequency during the growing period, the fertilization frequency during the growing period, and the pruning frequency during the growing period; the maintenance cycle during the dormant period includes the irrigation frequency during the dormant period, the fertilization frequency during the dormant period, and the pruning frequency during the dormant period.

[0097] In this embodiment, the system pre-establishes a seasonal plant growth cycle management model. Based on plant physiological rhythms and phenological characteristics, it subdivides the maintenance cycle into two main phases: growth and dormancy. By analyzing the correlation between historical meteorological data and plant growth data, it predicts the time points when plant growth states transition, and dynamically adjusts maintenance parameters accordingly.

[0098] Specifically, taking bermudagrass as an example, the system determines detailed maintenance frequency parameters based on its growth characteristics and the expected coverage requirement of 90%. The maintenance cycle during the growth period includes increased irrigation frequency, fertilization frequency, and pruning frequency; while the frequency is reduced during the dormant period.

[0099] S122. Determine the annual maintenance schedule based on the growth period maintenance cycle and the dormancy period maintenance cycle.

[0100] In this embodiment, a detailed maintenance schedule is automatically generated based on maintenance needs in different periods.

[0101] In one embodiment, the roof area feature information includes orientation, sunshine duration and shading conditions, referring to Figure 4 In step S200, the roof area characteristic information is obtained, the roof is divided into several areas according to the roof area characteristic information, and the area maintenance adjustment coefficient is obtained, which specifically includes the following steps:

[0102] S210: Divide the roof into several areas according to the sunshine duration and orientation, and obtain the illumination intensity coefficient of each area.

[0103] In this embodiment, the system uses lighting simulation analysis technology, combined with building information modeling, to construct a dynamic model of roof lighting throughout the year. Based on the geographical location, building orientation and surrounding environment, it calculates the changes in light intensity in various areas of the roof in different seasons and at different times, providing a scientific basis for regional division.

[0104] S220. Determine a partition maintenance adjustment coefficient based on the shading condition and the partition illumination intensity coefficient. The partition maintenance adjustment coefficient is used to perform regional adjustment on the overall maintenance parameter information.

[0105] In this embodiment, the system analyzes the impact of building obstructions on sunlight and combines this with the acquired zone-specific light intensity coefficients to calculate a maintenance adjustment factor. The system uses a weighted calculation model, taking into account factors such as the height, distance, and projection angle of the obstruction, to modify the light intensity coefficient and ultimately determine the maintenance adjustment factor for each zone.

[0106] In one embodiment, referring to Figure 5 In step S500, according to the abnormal warning and data collection instructions, the partition adjustment parameter information is obtained, which specifically includes the following steps:

[0107] S510. Obtaining zone environmental monitoring data and zone plant growth status data according to abnormal warning and data collection instructions.

[0108] In this embodiment, the system collects real-time environmental and growth status data through an environmental sensor network and plant growth monitoring equipment deployed in various areas of the roof.

[0109] Specifically, when the system receives an early warning of abnormal plant growth in the southwest region, it immediately activates the intensive monitoring mode for that area. For example, environmental monitoring equipment collects data every 30 minutes, and plant growth monitoring equipment collects morphological characteristics three times a day.

[0110] S520. Obtain an abnormality cause analysis based on the partitioned environmental monitoring data and the partitioned plant growth status data in combination with the growth difference value.

[0111] In this example, the system analyzes historical data to build a library of plant growth anomaly patterns, enabling rapid matching of current anomalies with known problem patterns. The system uses a decision tree algorithm to analyze the correlation between environmental factors and growth status, pinpointing the primary and secondary factors causing growth discrepancies.

[0112] S530: Determine the type and range of maintenance parameters that need to be adjusted based on the abnormality cause analysis and overall maintenance parameter information, and generate partition adjustment parameter information.

[0113] In this embodiment, the system automatically calculates the optimal adjustment plan for each maintenance parameter based on the abnormality cause analysis results and the plant physiological demand model. The priority and adjustment range of the parameter are dynamically determined based on the severity of the abnormality and the interaction between environmental factors.

[0114] Specifically, for example, in response to growth anomalies in the southwest region, the system generated a zoning parameter adjustment plan: first adjust the fertilization strategy, then optimize irrigation parameters, adjust pruning frequency, and so on.

[0115] In one embodiment, referring to Figure 6 In step S600, the maintenance plan adjustment instruction is triggered according to the partition adjustment parameter information, which specifically includes the following steps:

[0116] S610: Extracting a maintenance adjustment type from the partition adjustment parameter information. The maintenance adjustment type includes irrigation adjustment, fertilization adjustment, and pruning adjustment.

[0117] S620: For irrigation adjustment, calculate adjustment values ​​of irrigation water volume and irrigation duration based on soil moisture monitoring data.

[0118] S630: For fertilization adjustment, determine the adjustment values ​​of fertilizer ratio and fertilizer amount according to soil nutrient monitoring data.

[0119] S640: For pruning adjustment, determine adjustment values ​​of pruning height and pruning range according to plant growth density data.

[0120] S650: Compare each adjustment value with the plant tolerance range, generate a corresponding execution instruction sequence, and trigger a maintenance plan adjustment instruction.

[0121] In this embodiment, the system pre-establishes a plant tolerance assessment system, which includes a database of physiological characteristics of various plants, and assesses whether the adjustment parameters exceed the adaptation range of the plants.

[0122] Specifically, for example, the system compares the calculated adjustment values ​​with the tolerance range of bermudagrass: irrigation adjustment value -20% (tolerance range ±30%), fertilization adjustment value +20% (tolerance range ±25%), pruning adjustment value -12.5% ​​(tolerance range ±15%). If all are within the safe range, an execution instruction sequence will be generated and executed one by one in order of priority, and the plant response will be monitored in real time during the execution process.

[0123] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0124] Secondly, the present application provides a data-driven intelligent roof greening construction and maintenance system. The data-driven intelligent roof greening construction and maintenance system of the present application is described below in combination with the above-mentioned data-driven intelligent roof greening construction and maintenance method.

[0125] Reference Figure 7 , a data-driven intelligent green roof construction and maintenance system, including:

[0126] A greening scheme selection module is used to obtain greening scheme selection information and obtain overall maintenance parameter information based on the greening scheme selection information;

[0127] The area division module is used to obtain the roof area characteristic information, divide the roof into several functional areas according to the roof area characteristic information, and obtain the area maintenance adjustment coefficient;

[0128] Intelligent maintenance control module, used to trigger intelligent maintenance instructions based on the partition maintenance adjustment coefficient and overall maintenance parameter information;

[0129] The growth status monitoring module is used to obtain the growth status detection information of each partition, compare the growth status detection information of each partition with the preset standard growth parameters, obtain the growth difference value, compare the growth difference value with the preset change threshold value, and trigger an abnormal warning and data collection instruction if the growth difference value is outside the preset change threshold range;

[0130] Parameter adjustment module, used to obtain partition adjustment parameter information based on abnormal warning and data collection instructions;

[0131] The maintenance execution module is used to trigger maintenance plan adjustment instructions based on partition adjustment parameter information.

[0132] In one embodiment, the greening scheme selection module includes:

[0133] a parameter acquisition unit, configured to extract plant variety information from greening scheme selection information, wherein the greening scheme selection information includes plant variety, greening area, and expected coverage;

[0134] A database query unit, used to obtain the corresponding suitable temperature range, humidity range and light requirements according to the plant variety database;

[0135] A maintenance cycle calculation unit is used to obtain the maintenance cycle based on the greening area and expected coverage, combined with the growth characteristics of the plant species;

[0136] The parameter integration unit is used to associate and integrate the suitable temperature range, humidity range, light requirements and maintenance cycle to obtain overall maintenance parameter information, wherein the overall maintenance parameter information includes the suitable temperature range, humidity range, light requirements and maintenance cycle.

[0137] In one embodiment, the maintenance cycle calculation unit includes:

[0138] A cycle differentiation unit is used to obtain a growing period maintenance cycle and a dormant period maintenance cycle according to the growth characteristics and expected coverage of the plant variety, wherein the growing period maintenance cycle includes the growing period irrigation frequency, the growing period fertilization frequency, and the growing period pruning frequency, and the dormant period maintenance cycle includes the dormant period irrigation frequency, the dormant period fertilization frequency, and the dormant period pruning frequency;

[0139] The timetable generating unit is used to determine the annual maintenance plan timetable according to the growing period maintenance cycle and the dormant period maintenance cycle.

[0140] In one embodiment, the region division module includes:

[0141] A lighting analysis unit is used to divide the roof into several areas according to the sunshine duration and orientation, and obtain the lighting intensity coefficient of each area. The characteristic information of the roof area includes orientation, sunshine duration and shading;

[0142] The coefficient adjustment unit is used to determine the partition maintenance adjustment coefficient based on the shading situation and the partition light intensity coefficient. The partition maintenance adjustment coefficient is used to make regional adjustments to the overall maintenance parameter information.

[0143] In one embodiment, the parameter adjustment module includes:

[0144] A data acquisition unit is used to obtain zoned environmental monitoring data and zoned plant growth status data according to abnormal warnings and data collection instructions;

[0145] Cause analysis unit, used to obtain abnormal cause analysis based on the partitioned environmental monitoring data and the partitioned plant growth status data, combined with the growth difference value;

[0146] The parameter generation unit is used to determine the type and adjustment range of maintenance parameters that need to be adjusted based on the abnormal cause analysis and overall maintenance parameter information, and generate partition adjustment parameter information.

[0147] In one embodiment, the maintenance execution module includes:

[0148] A type identification unit is used to extract the maintenance adjustment type from the partition adjustment parameter information, where the maintenance adjustment type includes irrigation adjustment, fertilization adjustment, and pruning adjustment;

[0149] Irrigation adjustment unit, used to calculate the adjustment value of irrigation water volume and irrigation duration based on soil moisture monitoring data;

[0150] A fertilization adjustment unit is used to determine the adjustment value of fertilizer ratio and fertilizer amount according to soil nutrient monitoring data;

[0151] A pruning adjustment unit, used to determine adjustment values ​​of pruning height and pruning range according to plant growth density data;

[0152] The instruction generation unit is used to compare the various adjustment values ​​with the plant tolerance range, generate a corresponding execution instruction sequence, and trigger the maintenance plan adjustment instruction.

[0153] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown. The electronic device includes a processor, a memory and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a data-driven intelligent roof greening construction and maintenance method is implemented.

[0154] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0155] In one embodiment, an electronic device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0156] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The above-described computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0157] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A data-driven intelligent green roof construction and maintenance method, characterized in that: The steps include: Obtaining greening scheme selection information, and obtaining overall maintenance parameter information based on the greening scheme selection information; Obtaining roof area characteristic information, dividing the roof into a plurality of areas according to the roof area characteristic information, and obtaining a partition maintenance adjustment coefficient, wherein the roof area characteristic information includes orientation, sunshine duration, and shading; triggering intelligent maintenance instructions according to the partition maintenance adjustment coefficient and the overall maintenance parameter information; Acquire partition growth status detection information, compare the partition growth status detection information with preset standard growth parameters to obtain a growth difference value, compare the growth difference value with a preset change threshold value, and if the growth difference value is outside the preset change threshold range, trigger an abnormality warning and data collection instruction; Obtaining partition adjustment parameter information according to the abnormal warning and data collection instructions; According to the partition adjustment parameter information, trigger the maintenance plan adjustment instruction; The steps of obtaining roof area characteristic information, dividing the roof into several areas according to the roof area characteristic information, and obtaining the area maintenance adjustment coefficients specifically include the following steps: Divide the roof into several areas according to the sunshine duration and the orientation, and obtain the illumination intensity coefficient of each area, wherein the illumination intensity coefficient of each area is calculated and generated by a building information model and a dynamic illumination model throughout the year; According to the occlusion situation and the partition light intensity coefficient, the partition maintenance adjustment coefficient is determined. The partition maintenance adjustment coefficient is used to perform regional adjustment on the overall maintenance parameter information. The partition maintenance adjustment coefficient is corrected by a weighted calculation model of the occlusion height, distance and projection angle.

2. The data-driven intelligent green roof construction and maintenance method according to claim 1 is characterized in that: The greening scheme selection information includes plant species, greening area, and desired coverage, and the overall maintenance parameter information includes suitable temperature range, humidity range, lighting requirements, and maintenance cycle. Acquiring the greening scheme selection information and acquiring the overall maintenance parameter information based on the greening scheme selection information specifically includes the following steps: Extracting plant variety information from the greening scheme selection information, and obtaining corresponding suitable temperature range, humidity range and light requirements according to the plant variety database; Obtaining a maintenance cycle based on the greening area and desired coverage, combined with the growth characteristics of the plant species; The suitable temperature range, humidity range, light requirements and curing period are correlated and integrated to obtain the overall curing parameter information.

3. The data-driven intelligent green roof construction and maintenance method according to claim 2 is characterized in that: The maintenance cycle includes a growth period maintenance cycle and a dormancy period maintenance cycle. The maintenance cycle is obtained based on the greening area and the desired coverage, combined with the growth characteristics of the plant variety, and specifically includes the following steps: According to the growth characteristics and expected coverage of the plant variety, the growing period maintenance cycle and the dormant period maintenance cycle are obtained, wherein the growing period maintenance cycle includes the growing period irrigation frequency, the growing period fertilization frequency, and the growing period pruning frequency, and the dormant period maintenance cycle includes the dormant period irrigation frequency, the dormant period fertilization frequency, and the dormant period pruning frequency; An annual maintenance plan schedule is determined based on the growing period maintenance cycle and the dormant period maintenance cycle.

4. The data-driven intelligent green roof construction and maintenance method according to claim 1 is characterized in that: According to the abnormal warning and data collection instructions, the partition adjustment parameter information is obtained, which specifically includes the following steps: Acquire the zoned environmental monitoring data and zoned plant growth status data according to the abnormal warning and data collection instructions; Obtaining an abnormality cause analysis based on the sub-area environmental monitoring data and the sub-area plant growth status data in combination with the growth difference value; According to the abnormal cause analysis and the overall maintenance parameter information, the type of maintenance parameter to be adjusted and the adjustment range are determined, and the partition adjustment parameter information is generated.

5. The data-driven intelligent green roof construction and maintenance method according to claim 1 is characterized in that: According to the partition adjustment parameter information, a maintenance plan adjustment instruction is triggered, which specifically includes the following steps: Extracting a maintenance adjustment type from the partition adjustment parameter information, where the maintenance adjustment type includes irrigation adjustment, fertilization adjustment, and pruning adjustment; For irrigation adjustments, the adjustment values ​​of irrigation water volume and irrigation duration are calculated based on soil moisture monitoring data; For fertilization adjustments, determine the adjusted values ​​of fertilizer ratio and fertilizer amount based on soil nutrient monitoring data; For pruning adjustments, the adjustment values ​​of pruning height and pruning range are determined based on plant growth density data; Compare each adjustment value with the plant tolerance range, generate the corresponding execution instruction sequence, and trigger the maintenance plan adjustment instruction.

6. A data-driven intelligent green roof construction and maintenance system, characterized by: The data-driven intelligent green roof construction and maintenance method according to any one of claims 1 to 5 is applied, comprising: A greening scheme selection module is used to obtain greening scheme selection information and obtain overall maintenance parameter information based on the greening scheme selection information; A zone division module is used to obtain roof zone characteristic information, divide the roof into several functional zones according to the roof zone characteristic information, and obtain zone maintenance adjustment coefficients; An intelligent maintenance control module, configured to trigger intelligent maintenance instructions based on the partition maintenance adjustment coefficient and the overall maintenance parameter information; A growth status monitoring module is used to obtain partition growth status detection information, compare the partition growth status detection information with preset standard growth parameters to obtain a growth difference value, compare the growth difference value with a preset change threshold value, and trigger an abnormal warning and data collection instruction if the growth difference value is outside the preset change threshold range; A parameter adjustment module, configured to obtain partition adjustment parameter information according to the abnormal warning and data collection instructions; The maintenance execution module is used to trigger a maintenance plan adjustment instruction according to the partition adjustment parameter information.

7. The data-driven intelligent green roof construction and maintenance system according to claim 6 is characterized in that: The greening scheme selection module includes: a parameter acquisition unit, configured to extract plant variety information from the greening scheme selection information, wherein the greening scheme selection information includes plant variety, greening area, and expected coverage; A database query unit, used to obtain the corresponding suitable temperature range, humidity range and light requirements according to the plant variety database; a maintenance cycle calculation unit, configured to obtain a maintenance cycle based on the greening area and the desired coverage, in combination with the growth characteristics of the plant species; The parameter integration unit is used to associate and integrate the suitable temperature range, humidity range, light requirements and maintenance cycle to obtain the overall maintenance parameter information, wherein the overall maintenance parameter information includes the suitable temperature range, humidity range, light requirements and maintenance cycle.

8. An electronic device, characterized in that: The invention comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the data-driven intelligent roof greening construction and maintenance method described in any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the data-driven intelligent roof greening construction and maintenance method according to any one of claims 1 to 5 are implemented.

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