Urban garden greening management system
By designing an urban landscaping management system that integrates GIS mapping and analysis, monitoring and early warning, decision support, and data collection and processing, it solves the problem that existing systems are difficult to integrate and analyze multi-source data and lack of real-time monitoring and decision support, and achieves the optimal allocation of greening resources and the improvement of urban ecological functions.
Patent Information
- Application Number
- CN202411252225.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-09
- Publication Date
- 2025-06-03
AI Technical Summary
The existing urban landscaping management system is difficult to integrate and analyze multi-source data, and the lack of real-time monitoring and decision-making support tools, resulting in unoptimized allocation of greening resources and inefficient management.
Design an urban landscaping management system including GIS mapping and analysis module, monitoring and early warning module, decision support module and data collection and processing module. The system processes geospatial data through GIS technology, monitors environmental conditions in real time, provides decision support, and generates greening management strategies through correlation analysis.
It improves the efficiency and effectiveness of urban landscaping management, provides reasonable decision-making support, optimizes the allocation of greening resources, enhances urban ecological functions and biodiversity, and can predict and adapt to climate change.
Smart Images
Figure CN120087583A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of landscaping, and particularly to an urban landscaping management system. Background Art
[0002] Urban landscaping management systems are currently facing challenges and opportunities for rapid development. With the acceleration of urbanization, the requirements for the quality of urban greening are getting higher and higher, and at the same time, more efficient and intelligent management methods are needed to maintain and improve the urban ecological environment. At present, many systems still rely on traditional data collection and management models, which are often inefficient and difficult to adapt to the rapidly changing environment and complex urban ecological needs. Existing systems usually have difficulty integrating and analyzing large amounts of data from multiple sources, lack effective real-time monitoring means to timely detect and respond to various problems in greening areas, and existing management systems often lack precise decision support tools, resulting in sub-optimal allocation of greening resources and inability to achieve the best management effect.
[0003] Therefore, there is an urgent need to propose an urban landscaping system that can improve the efficiency and effect of urban landscaping management, provide reasonable decisions, and meet the needs of modern urban greening management. Summary of the Invention
[0004] The object of the present invention is to solve the technical problems in the above background, and propose an urban landscaping management system, including a GIS mapping and analysis module, a monitoring and warning module, a decision support module, and a data collection and processing module;
[0005] The GIS mapping and analysis module is the core module, which is used to handle all functions related to geospatial data. In this module, layers are created and managed through GIS technology;
[0006] The data collection and processing module collects data and converts it into the formats required by GIS layers and matrices;
[0007] The monitoring and warning module monitors the environmental conditions of greening areas in real time, and uses sensor networks and GIS layers to predict and identify potential problems;
[0008] The decision support module combines GIS analysis results, historical data, and model predictions to provide decision suggestions and solutions.
[0009] In a preferred solution, the layers include a water source layer, a vegetation layer, a soil layer, and a climate layer;
[0010] The water source layer includes the locations of reservoirs, rivers, and rainwater collection points;
[0011] The vegetation layer includes the distribution information of different types of plants;
[0012] The soil layer includes soil types and their characteristics;
[0013] The climate layer includes rainfall and temperature data.
[0014] In a preferred embodiment, for each layer, a boolean matrix is constructed, where the rows represent samples, the columns represent attributes, and each element in the matrix indicates whether the attribute exists in the sample.
[0015] In a preferred embodiment, the correlation degree between different layers is calculated, specifically including the following steps:
[0016] S1. Through a logical "AND" operation, find the item sets that appear in two or more layers, and generate connectable frequent item sets;
[0017] S2. For each connectable frequent item set, calculate its support and confidence in each layer;
[0018] S3. Use the confidence of the association rule to evaluate the correlation degree between different layers.
[0019] In a preferred embodiment, a greening management strategy is generated based on the calculated correlation degree, including: optimizing vegetation distribution, soil improvement, and adjusting irrigation plans.
[0020] In a preferred embodiment, the GIS mapping and analysis module identifies areas with suitable soil and moisture conditions through overlay analysis, thereby providing vegetation planting decisions.
[0021] In a preferred embodiment, the data collected by the data collection and processing module includes soil moisture, vegetation health status, and climate conditions.
[0022] In a preferred embodiment, the monitoring and early warning module predicts and evaluates the health status of the greening area by analyzing the layer data, thereby triggering an early warning and providing a basis for decision-making.
[0023] In a preferred embodiment, the calculation of support is specifically: count the number of times the item set appears in the dataset and compare it with the total data volume.
[0024] Confidence is a measure of the strength of an association rule, reflecting the likelihood that another condition holds given a certain condition. The calculation of confidence is specifically: P(A∩B) / P(A), where A and B are item sets.
[0025] In a preferred embodiment, the decision analysis module further includes a multi-decision analysis framework for comprehensively scoring different decisions and comparing and sorting the scoring results, specifically including the following steps:
[0026] S11. For each solution, calculate the benefit-cost ratio BCR:
[0027]
[0028] Among them, Benefits and Costs represent the standardized benefits and costs respectively, and Benefit Weights and Cost Weights represent the corresponding weights;
[0029] S22. Calculate the adaptability score:
[0030] Adjusted BCR = BCR × f(Adaptability Score)
[0031] S33. Combine the benefit-cost ratio and the adaptability score to calculate the comprehensive score for each scheme:
[0032] ComprehensiveScore = α × Adjusted BCR + (1 - α) × AdaptabilityScore
[0033] α is the weight of the adjusted BCR, usually determined according to the goals and strategies of the project. Adjusted BCR is the benefit-cost ratio adjusted according to the adaptability score, and Adaptability Score is the adaptability score of the project;
[0034] S44. Compare and rank all decisions according to the comprehensive score results.
[0035] The beneficial effects of the present invention are as follows: Through the GIS mapping and analysis module in the system of the present invention, managers can intuitively understand the spatial distribution and status of urban greening, enabling decisions to be based on accurate geospatial data. Using the Boolean matrix and correlation analysis, the system can identify key greening areas and needs, thereby more effectively allocating water resources, vegetation, and soil improvement resources. The monitoring and early warning module can monitor environmental conditions in real time, promptly discover and handle greening problems, reduce resource waste, and improve management efficiency. The decision support module selects the optimal greening decision by comprehensively considering costs, benefits, and adaptability scores, which is conducive to selecting long-term sustainable greening strategies. The integration of the climate layer enables the system to predict and adapt to the impact of climate change on urban greening, enhancing the resilience of the greening system. By optimizing the vegetation distribution and soil improvement, the system helps to protect and enhance the ecological functions and biodiversity of the city. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] Figure 1 is the flowchart of the urban landscaping system in the present invention. DETAILED DESCRIPTION OF THE INVENTION
[0037] Example 1
[0038] A rapidly developing medium-sized city is facing the problems of decreasing green coverage rate and intensifying urban heat island effect. Urban planners have decided to adopt an urban landscaping management system to optimize greening strategies and improve the quality of urban greening.
[0039] Implementation steps:
[0040] 1. System deployment and data collection:
[0041] Urban planners first deploy a sensor network within the city boundaries to collect climate data such as soil moisture, temperature, and rainfall.
[0042] Meanwhile, using GIS technology, water sources, vegetation, soil, and climate layers are created, digitizing existing geospatial data and importing it into the system.
[0043] 2. GIS mapping and analysis:
[0044] In the GIS mapping and analysis module, planners use GIS software to manage and analyze each layer, identifying greening potential areas and the distribution of existing greening resources.
[0045] Through overlay analysis, the system identifies areas with both suitable soil and sufficient water conditions, providing decision support for vegetation planting.
[0046] 3. Monitoring and early warning:
[0047] The monitoring and early warning module continuously monitors the environmental conditions of the greening areas. If it detects that the soil moisture in a certain area remains continuously below the normal level, the system automatically triggers an early warning, indicating a possible drought problem.
[0048] Based on the early warning information, planners promptly adjust the irrigation plan and increase the irrigation frequency in this area.
[0049] 4. Decision support:
[0050] The decision support module combines the GIS analysis results, historical data, and model predictions to propose a series of greening management strategies, including optimizing vegetation distribution, soil improvement, and adjusting irrigation plans.
[0051] Planners use Boolean matrices and correlation analysis to evaluate the potential benefits and costs of different strategies and select the best option for implementation.
[0052] 5. Implementation and adjustment:
[0053] Based on the strategies provided by the system, the city begins to implement greening projects, such as planting more drought-tolerant vegetation in the identified potential areas and improving the soil.
[0054] During the implementation process, the system continuously monitors the project effects and dynamically adjusts the greening management strategies according to the actual growth conditions and climate data.
[0055] 6. Benefit evaluation:
[0056] After the project is completed, the system evaluates the implementation effects through a multi-decision analysis framework, including vegetation coverage rate, the degree of mitigation of the urban heat island effect, and the utilization efficiency of greening resources.
[0057] Based on the evaluation results, planners provide feedback and make fine-tuning to the strategies proposed by the system to achieve continuous improvement and optimization.
[0058] By using the urban landscaping management system, the city has successfully increased the greening coverage rate, mitigated the urban heat island effect, and optimized the allocation and utilization of greening resources. Urban planners can make more accurate decisions based on real-time data and scientific analysis, improving the efficiency and effectiveness of urban greening management.
[0059] Example 2
[0060] During the process of constructing the Boolean matrix, the data of the water source layer, vegetation layer, soil layer, and climate layer are indeed used, but their representation forms and processing methods in the Boolean matrix will be different. The following is a detailed description of how to apply this data to construct the Boolean matrix:
[0061] 1. Water source layer
[0062] Data content: The locations of reservoirs, rivers, and rainwater collection points.
[0063] Boolean matrix application:
[0064] Rows: May represent different geographical locations or regions.
[0065] Columns: Represent whether there is a specific water source type (such as a reservoir, river).
[0066] Elements: If a certain location has a specific water source type, the corresponding matrix element value is 1 (true), otherwise it is 0 (false).
[0067] 2. Vegetation layer
[0068] Data content: Distribution information of different types of plants.
[0069] Boolean matrix application:
[0070] Rows: Represent different plots or vegetation areas.
[0071] Columns: Represent different plant species.
[0072] Element: If a specific plant species exists in a certain plot, the corresponding matrix element value is 1; otherwise, it is 0.
[0073] 3. Soil layer
[0074] Data content: Soil type and its characteristics.
[0075] Boolean matrix application:
[0076] Rows: Represent different plots or soil types.
[0077] Columns: Represent different soil properties (such as soil type, pH value, humidity, etc.).
[0078] Element: If a certain plot has a specific soil property, the corresponding matrix element value is 1; otherwise, it is 0.
[0079] 4. Climate layer
[0080] Data content: Rainfall and temperature data.
[0081] Boolean matrix application:
[0082] Rows: Represent different time periods or geographical locations.
[0083] Columns: Represent different climate conditions (such as high rainfall, high temperature).
[0084] Element: If a certain time period or location meets a specific climate condition, the corresponding matrix element value is 1; otherwise, it is 0.
[0085] Specific steps for constructing the Boolean matrix:
[0086] 1. Data preprocessing: Convert the original data into a format suitable for constructing the Boolean matrix. For example, discretize continuous climate data (such as rainfall, temperature), and define thresholds to determine when to mark a specific climate condition as existing.
[0087] 2. Matrix initialization: Initialize the Boolean matrix according to the number of samples and the types of attributes.
[0088] 3. Fill the matrix: Fill the matrix according to the existence of attributes for each sample. For example, if a certain plot has a specific plant species, then mark it as 1 in the corresponding column of the vegetation layer.
[0089] 4. Analysis preparation: Use the constructed Boolean matrix for further data analysis, such as frequent itemset mining, association rule analysis, etc.
[0090] In the above way, the Boolean matrix can convert complex geographical and environmental data into a form suitable for logical and set operations, thus supporting effective data mining and decision-making support.
[0091] Example 3
[0092] Calculate the correlation degree of different layers in this system, including the following steps:
[0093] S1. Through logical "AND" operation, find the item sets that appear in two or more layers, and generate connectable frequent item sets;
[0094] S2. For each connectable frequent item set, calculate its support degree and confidence degree in each layer;
[0095] S3. Use the confidence degree of association rules to evaluate the correlation degree between different layers.
[0096] S1: Through logical "AND" operation, find the item sets that appear in two or more layers, and generate connectable frequent item sets
[0097] The purpose of this step is to identify common features or patterns from multiple layers. In the urban landscaping management system, this can help us discover the vegetation types or other related factors that appear simultaneously under specific conditions (such as soil type, climate conditions). For example, we may find that a certain plant grows particularly well in moist loam soil.
[0098] Logical "AND" operation is a basic set operation used to determine the common elements between two or more sets.
[0099] In GIS and data mining, this is usually achieved by comparing the attribute values of layers to find specific features that appear in all relevant layers.
[0100] S2: For each connectable frequent item set, calculate its support degree and confidence degree in each layer.
[0101] Support degree measures the frequency of an item set appearing in all data. In landscaping, a high support degree means that a specific vegetation-soil combination is prevalent in multiple locations.
[0102] Confidence degree measures the conditional probability of the subsequent item appearing given the prior item. In landscaping, this can help us understand the reliability of vegetation growth under specific soil types.
[0103] Support degree calculation usually involves counting the number of times an item set appears in the dataset and comparing it with the total data volume.
[0104] Confidence degree calculation needs to consider causal or associative relationships, usually expressed as conditional probability, such as P(A∩B) / P(A), where A and B are item sets.
[0105] S3: Use the confidence of the association rule to evaluate the degree of association between different layers
[0106] This step is to evaluate the association strength between different layers in a quantitative way. In the landscaping management system, this helps to identify which factors (such as soil type, climate conditions) have the greatest impact on vegetation growth, thus providing a scientific basis for decision-making.
[0107] Confidence is a measure of the strength of an association rule, which reflects the likelihood that another condition (such as vegetation type) holds given a certain condition (such as soil type).
[0108] In the association analysis of multiple layers, rules with high confidence indicate that under specific conditions, the co-occurrence of certain events or features is very strong, which may indicate important ecological or environmental relationships.
[0109] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. An urban gardening and greening management system, characterized by: It includes GIS mapping and analysis module, monitoring and early warning module, decision support module and data collection and processing module; The GIS Mapping and Analysis module is the core module, which is used to create and manage layers using GIS technology; The data collection and processing module collects data and converts the data into the required format of GIS layers and matrices; The monitoring and early warning module monitors the environmental conditions of green areas in real time, using sensor networks and GIS layers to predict and identify potential problems; The decision support module combines GIS analysis results, historical data and model predictions to provide decision recommendations and solutions.
2. According to claim 1, an urban landscaping management system is characterized by: GIS layers include water source layer, vegetation layer, soil layer and climate layer; The water source layer includes the locations of reservoirs, rivers, and rainwater collection points; The vegetation layer includes the distribution information of different types of plants; The soil layer includes soil types and their properties; Climate layers include rainfall and temperature data.
3. According to claim 1, the urban landscaping management system is characterized by: For each GIS layer, a Boolean matrix is constructed, where the rows represent samples and the columns represent attributes. Each element in the matrix indicates whether the attribute exists in the sample.
4. According to claim 3, an urban landscaping management system is characterized by: Calculating the correlation between different GIS layers includes the following steps: S1. Through logical "AND" operation, find out the itemsets that appear in two or more layers and generate connectable frequent itemsets; S2. For each connectable frequent item set, calculate its support and confidence in each GIS layer; S3. Use the confidence of association rules to evaluate the association between different layers.
5. According to claim 4, an urban landscaping management system is characterized by: Greening management strategies are generated based on the calculated correlation, including: optimizing vegetation distribution, soil improvement, and adjusting irrigation plans.
6. According to claim 1, the urban landscaping management system is characterized by: The GIS mapping and analysis module identifies areas with suitable soil and water conditions through overlay analysis, thereby providing vegetation planting decisions.
7. According to claim 1, the urban landscaping management system is characterized by: The data collection and processing module collects data including soil moisture, vegetation health and climate conditions.
8. According to claim 1, the urban landscaping management system is characterized by: The monitoring and early warning module predicts and evaluates the health status of green areas by analyzing GIS layer data, thereby triggering early warnings and providing a basis for decision-making.
9. The urban landscaping management system according to claim 4 is characterized by: The calculation of support is as follows: count the number of times the item set appears in the data set and compare it with the total amount of data; Confidence is a measure of the strength of association rules, reflecting the possibility that another condition is true when one condition is given. The calculation of confidence is: P(A∩B) / P(A), where A and B are item sets.
10. The urban landscaping management system according to claim 1 is characterized by: The decision analysis module also includes a multi-decision analysis framework for comprehensively scoring different decisions and comparing and ranking the scoring results, which specifically includes the following steps: S11. For each option, calculate the benefit-cost ratio BCR: Among them, Benefits and Costs represent the standardized benefits and costs respectively, and Benefit Weights and CostWeights represent the corresponding weights; S22. Calculate the adaptability score: Adjusted BCR=BCR×f(Adaptability Score); S33. Combine the benefit-cost ratio and adaptability score to calculate the overall score for each option: ComprehensiveScore=α×Adjusted BCR+(1-α)×AdaptabilityScore; α is the weight of the adjusted BCR, which is usually determined based on the project's goals and strategies. Adjusted BCR is the benefit-cost ratio adjusted based on the adaptability score. Adaptability Score is the adaptability score of the project. S44. Compare and rank all decisions based on the comprehensive scoring results.