Smart garden management method and system based on big data technology

Abstract

The invention provides a smart garden management method and system based on a big data technology. The method comprises the steps of collecting multi-source heterogeneous data in a garden in real time through an environment sensing network, and performing integration and comparison processing on the multi-source heterogeneous data to obtain a garden ecological portrait; the current garden ecological health condition is evaluated by using the garden ecological portrait, the ecological change trend possibly occurring in a short period is predicted, and an ecological change trend prediction report is generated; according to the ecological change trend prediction report, searching an optimal solution for automatic adjustment, and generating a resource allocation scheme and a touring experience optimization plan; and based on the resource configuration scheme and the touring experience optimization plan, establishing a continuous learning mechanism, implementing a regular review process, and generating management system configuration. According to the technical scheme, the garden management efficiency and the resource utilization rate are remarkably improved, and meanwhile the overall satisfaction degree of tourists is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of big data technology, and in particular to a smart garden management method and system based on big data technology. Background Art

[0002] With the acceleration of urbanization, smart garden management has become an important means to improve the quality of urban environment and the quality of life of residents. Modern gardens not only need to maintain plant growth and ecological balance, but also need to take into account visitor experience and efficient resource utilization. To this end, a smart garden management method based on big data technology is needed, which can collect and process multi-source heterogeneous data in real time, combine geographic information system (GIS) and time series analysis, form a dynamic garden ecological portrait, and provide support for scientific decision-making.

[0003] Currently, most garden management systems rely on traditional manual data collection and analysis methods supplemented by simple automation tools. Although these systems can monitor the status of gardens to a certain extent, they lack the ability to respond in real time to complex and changing environments. In addition, existing solutions usually use static models for prediction and adjustment, and fail to make full use of big data technology and advanced machine learning algorithms, such as Bayesian networks and particle swarm optimization (PSO), to achieve intelligent and adaptive management strategies.

[0004] The existing garden management system has the following main defects: first, data collection is incomplete and updates are delayed, which cannot accurately reflect real-time changes; second, there is a lack of a comprehensive evaluation system, which makes it difficult to effectively predict short-term ecological change trends; third, the automatic adjustment mechanism is insufficient, and resource allocation and visitor guidance strategies cannot be flexibly optimized according to actual conditions. These problems limit the efficiency and accuracy of garden management, resulting in resource waste and poor visitor experience. Summary of the invention

[0005] The embodiments of the present application provide a smart garden management method and system based on big data technology, which are used to solve the problems of delayed data collection, imperfect evaluation system and insufficient automatic adjustment mechanism in garden management in the prior art.

[0006] In a first aspect, the present application provides a smart garden management method based on big data technology, including: The multi-source heterogeneous data in the garden are collected in real time through the environmental perception network, and the multi-source heterogeneous data are integrated and compared with the geographic information system and time series analysis to obtain the ecological portrait of the garden; The garden ecological portrait is used to evaluate the current ecological health status of the garden, and the Bayesian network algorithm is used to predict the ecological change trend that may occur in the short term. The multi-criteria decision analysis method is introduced in combination with the scenario simulation technology to generate an ecological change trend forecast report; According to the ecological change trend forecast report, the maintenance operation time and maintenance work intensity as well as the tourist guidance strategy in the garden are automatically adjusted, and the particle swarm optimization algorithm is used to find the optimal solution for automatic adjustment, and based on the garden ecological health and tourist experience analysis in the garden ecological portrait, a resource allocation plan and a tour experience optimization plan are generated; Based on the resource allocation plan and tour experience optimization plan, establish a continuous learning mechanism, implement a regular review process, and generate a management system configuration.

[0007] Optionally, the dynamic garden ecological portrait is used to evaluate the current garden ecological health status, and the Bayesian network algorithm is used to predict the ecological change trend that may occur in the short term, and a multi-criteria decision analysis method is introduced in combination with scenario simulation technology to generate an ecological change trend forecast report, including: Using the multi-source heterogeneous data and long-term trend and seasonal pattern recognition results in the dynamic garden ecological portrait, a comprehensive assessment is performed on the current garden ecological health status to obtain the current ecological health status assessment result; Based on the current ecological health status assessment results, a Bayesian network algorithm is used to perform probabilistic prediction processing on the ecological change trends that may occur in the short term, and a short-term ecological change trend prediction model is obtained; According to the short-term ecological change trend prediction model, the evaluation criteria weights for specific management measures such as irrigation plans, fertilization plans, pest control measures and visitor guidance strategies in the garden are set, and the effects of the specific management measures are quantitatively evaluated through a multi-criteria decision analysis method to generate quantitative evaluation results; Based on the quantitative evaluation results, using scenario simulation technology to create virtual scenarios under different specific management measures, test and compare the performance of the different specific management measures under various conditions, and obtain specific management measure recommendations; Integrate the specific management measures and short-term ecological change trend prediction model to generate an ecological change trend prediction report.

[0008] Optionally, according to the short-term ecological change trend prediction model, the evaluation criteria weights under specific management measures such as irrigation plans, fertilization plans, pest control measures and visitor guidance strategies in the garden are set, and the effects of the specific management measures are quantitatively evaluated through a multi-criteria decision analysis method to generate quantitative evaluation results, including: Based on the short-term ecological change trend prediction model, key factors affecting the ecological health of the garden are identified and specific management measures are obtained; Based on the key factors and specific management measures, define the evaluation criteria of the specific management measures, and assign corresponding weight values ​​to each evaluation criterion to obtain the evaluation criterion weight configuration; Using the weight configuration of the evaluation criteria, combined with the data provided by the short-term ecological change trend prediction model, a multi-criteria decision analysis method is applied to conduct a comprehensive quantitative evaluation of the effects of the specific management measures to generate preliminary quantitative evaluation results; Based on the preliminary quantitative evaluation results, sensitivity analysis and robustness testing are carried out to generate quantitative evaluation results, taking into account the constraints and uncertainties that may be encountered in actual operations.

[0009] Optionally, based on the quantitative evaluation results, scenario simulation technology is used to create virtual scenarios under different specific management measures, and the performance of these specific management measures under various conditions is tested and compared to obtain specific management measures recommendations, including: Based on the quantitative evaluation results, representative specific management measures and the weight configuration of the evaluation criteria are selected, and scenario construction processing is performed on the specific management measures to obtain multiple representative specific management measures; Using the multiple representative specific management measures, combined with actual environmental parameters and historical data in the garden ecological portrait, scenario simulation technology is applied to create different virtual scenes, and each virtual scene corresponds to a management measure implementation plan, to obtain multiple sets of virtual scene sets; According to the multiple sets of virtual scene sets, different environmental conditions and operating parameters are set, simulation tests are performed on the performance of each specific management measure under different conditions, test data of the simulation tests are collected, and simulation test results are obtained; Based on the simulation test results, the effects of different specific management measures under various environmental conditions are compared and analyzed, the optimal or suboptimal specific management measure solutions are identified, and specific management measure recommendations are generated.

[0010] Optionally, according to the ecological change trend forecast report, the maintenance operation time and maintenance work intensity and the tourist guidance strategy in the garden are automatically adjusted, and the particle swarm optimization algorithm is used to find the optimal solution for automatic adjustment, and based on the garden ecological health and tourist experience analysis in the garden ecological portrait, a resource allocation plan and a tour experience optimization plan are generated, including: Utilizing the ecological change trend forecast report, the current and short-term future ecological conditions and potential changes are analyzed and processed, the types of maintenance operations and tourist guidance strategies that need to be adjusted are identified, and a list of adjustment requirements is obtained; According to the adjustment demand list, combined with the historical maintenance operation time and maintenance work intensity, the particle swarm optimization algorithm is applied to automatically adjust the maintenance operation time and maintenance work intensity and the tourist guidance strategy under the premise of meeting the resource constraint conditions, find the optimal solution for automatic adjustment, and generate a preliminary adjustment plan; Based on the preliminary adjustment plan, the garden ecological health data in the garden ecological portrait is used to evaluate the effect of the adjusted maintenance operation to obtain an ecological protection evaluation result; Based on the analysis of the visitor experience in the garden ecological portrait, the influence of the adjusted visitor guidance strategy on visitor satisfaction is evaluated to obtain the visitor experience evaluation result; Integrate the ecological protection assessment results and the tourist experience assessment results, further optimize the preliminary adjustment plan, and generate a resource allocation plan and a tour experience optimization plan.

[0011] Optionally, the analysis of the visitor experience in the garden ecological portrait is used to evaluate the impact of the adjusted visitor guidance strategy on visitor satisfaction, and obtain a visitor experience evaluation result, including: Utilizing the tourist experience data in the garden ecological portrait, extracting multiple indicators related to tourist satisfaction, and forming a tourist experience indicator system; According to the tourist experience index system and the adjusted tourist guidance strategy, the tourist flow of each scenic spot and area in the garden is predicted and processed, the tourist flow in different time periods is simulated, and the tourist flow simulation results are generated; Based on the tourist flow simulation results, a user experience scoring model is applied to comprehensively consider factors such as tourist stay time, queue waiting time, and crowding level, calculate the tourist satisfaction score of each attraction or area, and obtain a preliminary tourist satisfaction assessment; The preliminary visitor satisfaction evaluation is used in combination with historical visitor feedback data and social media evaluation information to conduct multi-source data analysis, calibrate and optimize the visitor satisfaction score, and generate a visitor experience evaluation result.

[0012] Optionally, based on the resource allocation scheme and the tour experience optimization plan, a continuous learning mechanism is established, a regular review process is implemented, and a management system configuration is generated, including: Execute the resource allocation plan and tour experience optimization plan, track and monitor the actual implementation effect, collect multi-source data on maintenance operation execution, resource utilization efficiency and tourist feedback, and obtain implementation effect monitoring data; Based on the execution effect monitoring data and combined with the dynamic change information in the garden ecological portrait, an intelligent decision-making algorithm is applied to analyze the deviation between the actual execution effect and the expected execution effect, identify areas where the execution effect needs to be improved, and generate a deviation analysis report; Based on the deviation analysis report, design and implement a regular review process to review the effectiveness of resource allocation plans and tour experience optimization plans, and obtain regular review results; The periodic review results are used to update parameter settings in the intelligent decision-making algorithm, adjust resource allocation and tourist guidance strategies, introduce impact assessments of socio-economic factors, and generate management system configurations.

[0013] In a second aspect, the present application embodiment provides a smart garden management system based on big data technology, including: The acquisition module is used to collect multi-source heterogeneous data in the garden in real time through the environmental perception network, and integrate and compare the multi-source heterogeneous data in combination with the geographic information system and time series analysis to obtain the garden ecological portrait; An evaluation module is used to evaluate the current ecological health status of the garden by using the garden ecological portrait, and to predict the ecological change trend that may occur in the short term by applying the Bayesian network algorithm, and to generate an ecological change trend prediction report by introducing a multi-criteria decision analysis method combined with scenario simulation technology; An adjustment module is used to automatically adjust the maintenance operation time and maintenance work intensity and the tourist guidance strategy in the garden according to the ecological change trend forecast report, use a particle swarm optimization algorithm to find the optimal solution for automatic adjustment, and generate a resource allocation plan and a tour experience optimization plan based on the garden ecological health and tourist experience analysis in the garden ecological portrait; A module is established to establish a continuous learning mechanism, implement a regular review process, and generate a management system configuration based on the resource allocation plan and tour experience optimization plan.

[0014] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a smart garden management method based on big data technology as described in the first aspect.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium, characterized in that a computer program is stored therein, and when the computer program is executed by a computer, a smart garden management method based on big data technology as described in the first aspect is implemented.

[0016] In an embodiment of the present application, multi-source heterogeneous data in the garden are collected in real time through an environmental perception network, and the multi-source heterogeneous data are integrated and compared in combination with a geographic information system and time series analysis to obtain a garden ecological portrait; the garden ecological portrait is used to evaluate the current ecological health status of the garden, and the Bayesian network algorithm is used to predict the ecological change trend that may occur in the short term, and a multi-criteria decision analysis method is introduced in combination with scenario simulation technology to generate an ecological change trend forecast report; according to the ecological change trend forecast report, the maintenance operation time and maintenance work intensity as well as the tourist guidance strategy in the garden are automatically adjusted, and a particle swarm optimization algorithm is used to find the optimal solution for automatic adjustment, and based on the garden ecological health and tourist experience analysis in the garden ecological portrait, a resource allocation plan and a tour experience optimization plan are generated; based on the resource allocation plan and the tour experience optimization plan, a continuous learning mechanism is established, a regular review process is implemented, and a management system configuration is generated.

[0017] The technical solution of this application has the following beneficial effects: Through the real-time collection of multi-source heterogeneous data through the environmental perception network, combined with the geographic information system (GIS) and time series analysis, comprehensive and real-time monitoring of the garden's ecological status is ensured, and the accuracy and timeliness of the data are improved; the Bayesian network algorithm is used to predict the ecological change trends that may occur in the short term, and the multi-criteria decision analysis method is introduced in combination with scenario simulation technology to generate a detailed ecological change trend forecast report. This not only improves the accuracy of the prediction, but also provides managers with a scientific basis for decision-making; according to the ecological change trend forecast report, the particle swarm optimization (PSO) algorithm is used to automatically adjust the maintenance operation time and intensity and the tourist guidance strategy to find the optimal solution. This method can dynamically respond to environmental changes, ensure the efficiency and flexibility of resource allocation, and optimize the tourist experience and improve overall satisfaction.

[0018] Furthermore, by utilizing multi-source heterogeneous data and long-term trend and seasonal pattern recognition results in dynamic garden ecological portraits, the present invention achieves a comprehensive and integrated assessment of the current garden ecological health status, and applies the Bayesian network algorithm to perform probabilistic prediction of short-term ecological change trends, combined with multi-criteria decision analysis methods and scenario simulation technology, to generate a detailed ecological change trend forecast report. This method not only improves the accuracy and timeliness of evaluation and prediction, but also optimizes irrigation plans, fertilization plans, pest and disease control measures, and tourist guidance strategies by quantitatively evaluating the effects of specific management measures. Finally, by integrating specific management measures recommendations with short-term ecological change trend forecast models, a scientific and systematic ecological change trend forecast report is formed, which significantly improves the level of intelligence in garden management and the scientific nature of decision-making, ensures the effective allocation of resources and the continuous optimization of tourist experience, and effectively solves the problems of imperfect evaluation systems and insufficient automatic adjustment mechanisms in existing solutions.

[0019] Furthermore, according to the ecological change trend forecast report, the present invention realizes the intelligent automatic adjustment of the maintenance operation time and intensity and the tourist guidance strategy, adopts the particle swarm optimization algorithm to find the optimal solution under the resource constraint condition, and generates the resource allocation plan and the tour experience optimization plan based on the ecological health and tourist experience analysis in the garden ecological portrait. This method can not only accurately identify the current and future short-term ecological conditions and potential changes, dynamically adjust the maintenance operation type and tourist guidance strategy, but also ensure the dual optimization of ecological protection and tourist experience by evaluating the adjusted maintenance operation effect and tourist satisfaction. Finally, the ecological protection evaluation results and the tourist experience evaluation results are integrated to further optimize the preliminary adjustment plan, which significantly improves the scientificity and flexibility of garden management, effectively solves the problem of insufficient automatic adjustment mechanism in the existing scheme, and realizes efficient allocation of resources and continuous improvement of tourist experience.

[0020] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A flowchart of a smart garden management method based on big data technology provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a smart garden management system based on big data technology provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0024] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0025] 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, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0026] Figure 1 A flowchart of a smart garden management method based on big data technology is provided for the embodiment of the present application. Figure 1 As shown, the method includes: 101. Collect multi-source heterogeneous data in the garden in real time through the environmental perception network, integrate and compare the multi-source heterogeneous data in combination with the geographic information system and time series analysis, and obtain the ecological portrait of the garden; In this step, multi-source heterogeneous data in the garden are collected in real time through the environmental perception network, including but not limited to meteorological data (temperature, humidity, precipitation, etc.), soil data (water content, pH value, nutrient level, etc.), plant growth data (height, leaf area index, etc.) and visitor behavior data (flow, residence time, etc.). Combined with the geographic information system (GIS) and time series analysis technology, these data are integrated and compared to form a "garden ecological portrait" that comprehensively reflects the ecological status of the garden.

[0027] In the embodiment of the present application, a variety of sensors and monitoring equipment are first deployed, such as weather stations, soil moisture sensors, cameras, etc., to collect different types of multi-source heterogeneous data in the garden in real time. Then, the GIS system is used to combine spatial information with time series data, perform data cleaning, format conversion and standardization processing, and ensure data consistency and availability. Finally, the multi-source heterogeneous data is integrated and compared through data analysis algorithms to generate a dynamically updated garden ecological portrait, providing a basis for subsequent evaluation and prediction.

[0028] Suppose a city park has installed multiple smart sensor nodes that collect data about weather, soil conditions, and visitor traffic every minute. Through the GIS platform, managers can visually view the distribution of this data on the map and use time series analysis tools to identify long-term trends and seasonal patterns. For example, during the high temperatures of summer, the system detects a rapid decrease in soil moisture in certain areas, indicating the need to increase irrigation frequency. This enables managers to adjust maintenance strategies in a timely manner to ensure plant health.

[0029] 102. Using the garden ecological portrait, evaluate the current garden ecological health status, apply the Bayesian network algorithm to predict the ecological change trend that may occur in the short term, introduce multi-criteria decision analysis method combined with scenario simulation technology, and generate an ecological change trend forecast report; In this step, the above-generated garden ecological portrait is combined with the historical data and real-time data of the current garden ecological health status, and the Bayesian network algorithm is used to perform probabilistic prediction processing on the ecological change trends that may occur in the short term. At the same time, multi-criteria decision analysis methods and scenario simulation technology are introduced to comprehensively consider the influence of multiple factors and generate a detailed ecological change trend forecast report to guide future management decisions.

[0030] In the embodiment of the present application, based on the multi-source heterogeneous data in the garden ecological portrait, the current garden ecological health status is first comprehensively evaluated to obtain a quantitative score. Then, the Bayesian network algorithm is applied to combine historical data and real-time data to predict the ecological change trends that may occur in the short term in the future. Next, a multi-criteria decision analysis method is used to evaluate the effects of various management measures, and virtual scenarios under different management schemes are created through scenario simulation technology to test their performance in various situations, and finally an ecological change trend forecast report containing specific suggestions is generated.

[0031] Suppose a smart garden system generates the latest ecological portrait based on multi-source heterogeneous data from the past month, showing that vegetation in some areas is wilting. The system uses the Bayesian network algorithm to predict the risk of more severe drought in the next two weeks. Subsequently, different irrigation plans are evaluated by multi-criteria decision analysis methods, and the effects of different irrigation strategies are compared in combination with scenario simulation technology. The results show that the use of smart drip irrigation systems can effectively improve vegetation conditions while saving water resources. Based on this, the system generates a detailed ecological change trend forecast report and recommends the immediate implementation of smart drip irrigation systems.

[0032] 103. According to the ecological change trend forecast report, the maintenance operation time and maintenance work intensity as well as the tourist guidance strategy in the garden are automatically adjusted, and the particle swarm optimization algorithm is used to find the optimal solution for automatic adjustment, and based on the garden ecological health and tourist experience analysis in the garden ecological portrait, a resource allocation plan and a tour experience optimization plan are generated; In this step, the types of maintenance operations and tourist guidance strategies that need to be adjusted are identified based on the ecological change trend forecast report, and a list of adjustment requirements is obtained. Combined with historical maintenance operation time and intensity data, the particle swarm optimization (PSO) algorithm is applied to automatically adjust the maintenance operation time and intensity and tourist guidance strategy under the condition of satisfying resource constraints, find the optimal solution, and generate a preliminary adjustment plan. Furthermore, based on the garden ecological health data and tourist experience analysis, the preliminary adjustment plan is evaluated and optimized, and finally a resource allocation plan and a tour experience optimization plan are generated.

[0033] In the embodiment of the present application, the ecological change trend forecast report is first analyzed to determine the specific maintenance operations and tourist guidance strategies that need to be adjusted, and a list of adjustment requirements is formed. Then, combined with historical data, the PSO algorithm is applied to find the best adjustment plan under resource constraints to ensure the maximization of maintenance operation efficiency and tourist satisfaction. Next, based on the ecological health and tourist experience data in the garden ecological portrait, the effect of the preliminary adjustment plan is evaluated, and it is optimized according to the evaluation results to generate the final resource allocation plan and tour experience optimization plan.

[0034] Suppose a smart garden system predicts that there is a risk of heavy rainfall in the next week, which may cause waterlogging in some low-lying areas. Based on this prediction, the system identifies the need for early drainage and enhanced safety warnings, and forms a list of adjustment requirements. Combining historical drainage records and work intensity data, the system uses the PSO algorithm to find the optimal drainage path and schedule, while adjusting the tourist guidance strategy and increasing the number of safety signs. Finally, the system generates a resource allocation plan and a tour experience optimization plan to ensure garden safety and tourist convenience during the rainy season.

[0035] 104. Based on the resource allocation plan and tour experience optimization plan, establish a continuous learning mechanism, implement a regular review process, and generate a management system configuration.

[0036] In this step, based on the resource allocation plan and tour experience optimization plan generated in the previous step, a continuous learning mechanism is established to regularly review and evaluate the actual implementation results, continuously adjust and optimize the management strategy, and ensure the adaptability and advancement of the system. This step also includes generating new management system configurations to better respond to changing ecological environments and tourist needs in future operations.

[0037] In the embodiment of the present application, specific management measures are first implemented according to the resource allocation plan and the tour experience optimization plan. Then, the actual implementation effect is reviewed regularly, and deviations are identified and reasons are analyzed by comparing the expected goals and actual results. On this basis, the machine learning algorithm is used to continuously optimize the model parameters and improve the management strategy. Finally, a new management system configuration is generated based on the optimized strategy to ensure that the system can flexibly respond to future challenges and maintain efficient operation.

[0038] Suppose a smart garden system implements a new resource allocation plan and a tour experience optimization plan. Regular reviews reveal that visitor satisfaction in a certain area is lower than expected. System analysis finds that the unreasonable seating layout in this area makes it inconvenient for visitors to rest. To this end, the system uses machine learning algorithms to re-optimize the seating layout and adjust the visitor guidance route. After several iterations of optimization, visitor satisfaction has significantly improved. The system generates a new management system configuration based on the optimized strategy to ensure that it can better meet visitor needs in future operations.

[0039] In summary, steps 101 to 104 cover the complete process from data collection, ecological assessment and prediction, automatic adjustment and optimization to continuous learning, aiming to provide an intelligent and adaptive smart garden management system to meet the needs of efficient resource management and high-quality visitor experience. Through real-time monitoring and intelligent optimization, the system not only improves the maintenance level of garden ecological health, but also enhances the satisfaction and sense of security of tourists, and realizes scientific and meticulous garden management.

[0040] In order to solve the problem of accuracy in predicting ecological change trends, in some embodiments, the dynamic garden ecological portrait is used in step 102 to evaluate the current garden ecological health status, and the Bayesian network algorithm is used to predict the ecological change trends that may occur in the short term, and a multi-criteria decision analysis method is introduced in combination with scenario simulation technology to generate an ecological change trend prediction report, including: The multi-source heterogeneous data and long-term trend and seasonal pattern recognition results in the dynamic garden ecological portrait are used to conduct a comprehensive assessment of the current ecological health status of the garden to obtain the current ecological health status assessment results; based on the current ecological health status assessment results, the Bayesian network algorithm is used to perform probabilistic prediction processing on the ecological change trends that may occur in the short term to obtain a short-term ecological change trend prediction model; according to the short-term ecological change trend prediction model, the evaluation criteria weights under the specific management measures of the irrigation plan, fertilization plan, pest and disease control measures and visitor guidance strategy in the garden are set, and the effects of the specific management measures are quantitatively evaluated through a multi-criteria decision analysis method to generate a quantitative evaluation result; based on the quantitative evaluation results, scenario simulation technology is used to create virtual scenes under different specific management measures, and the performance of the different specific management measures in various situations is tested and compared to obtain specific management measure recommendations; the specific management measure recommendations are integrated with the short-term ecological change trend prediction model to generate an ecological change trend prediction report.

[0041] In this embodiment, the multi-source heterogeneous data in the dynamic garden ecological portrait (such as meteorological, soil, plant growth and tourist behavior data) and the long-term trend and seasonal pattern recognition results are used to comprehensively evaluate the current garden ecological health status and obtain the current ecological health status evaluation results; based on these evaluation results, the Bayesian network algorithm is used to probabilistically predict the ecological change trends that may occur in the short term, and generate a short-term ecological change trend prediction model; according to the prediction model, the evaluation criteria weights under specific management measures such as irrigation plans, fertilization plans, pest and disease control measures and tourist guidance strategies are set, and the effects of specific management measures are quantitatively evaluated through multi-criteria decision analysis methods; scenario simulation technology is used to create virtual scenes under different management measures, test and compare the performance of these measures in various situations, and finally integrate specific management measures suggestions with the short-term ecological change trend prediction model to generate a detailed ecological change trend prediction report.

[0042] In the embodiment of the present application, first, the system uses multi-source heterogeneous data and historical trend analysis to evaluate the current ecological health status of the garden and obtain a comprehensive evaluation result; secondly, based on the evaluation results, the system applies the Bayesian network algorithm to predict the possible change trends in the short term and establishes a short-term ecological change trend prediction model; thirdly, the system sets the evaluation criteria weights of specific management measures according to the prediction model, and quantitatively evaluates the effectiveness of each measure through a multi-criteria decision analysis method; finally, the system uses scenario simulation technology to create virtual scenes under different management measures, compares the performance of various measures, and integrates all information to generate a final ecological change trend prediction report.

[0043] Here is a specific example: Suppose an urban park has introduced an advanced intelligent monitoring system to collect data such as temperature, humidity, soil moisture, vegetation growth, and visitor flow in real time. First, the system uses these data combined with long-term trend and seasonal pattern recognition results to assess the current ecological health of the park, and finds that vegetation in some areas shows signs of withering, and frequent visitor activities lead to excessive local pressure; secondly, the system uses the Bayesian network algorithm to predict the possible drought risk in the next two weeks, and establishes a short-term ecological change trend prediction model; thirdly, the system sets the evaluation criteria weights for specific management measures for irrigation, fertilization, pest control, and visitor guidance based on the prediction model, and quantitatively evaluates the effect of each measure through a multi-criteria decision analysis method. For example, the water-saving effect of the intelligent drip irrigation system is significantly better than that of the traditional sprinkler irrigation method; finally, the system uses scenario simulation technology to create virtual scenes under different irrigation strategies and test their performance under drought conditions. The results show that the intelligent drip irrigation system not only improves water resource utilization, but also effectively improves the health of vegetation. Through the above steps, the system generates a detailed ecological change trend prediction report, recommends the immediate implementation of the intelligent drip irrigation system to cope with the upcoming drought risk, and adjusts the visitor guidance strategy to alleviate the problem of excessive local pressure.

[0044] In summary, this series of steps ensures the accuracy and scientific nature of the prediction of ecological change trends, provides specific suggestions for management measures, and achieves efficient allocation of resources and continuous optimization of tourist experience.

[0045] In order to solve the problem of comprehensiveness and accuracy in the evaluation of specific management measures, in some embodiments, the evaluation criteria weights for specific management measures such as irrigation plans, fertilization plans, pest control measures, and visitor guidance strategies in the garden are set according to the short-term ecological change trend prediction model, and the effects of the specific management measures are quantitatively evaluated through a multi-criteria decision analysis method to generate quantitative evaluation results, including: Based on the short-term ecological change trend prediction model, the key factors affecting the ecological health of the garden are identified to obtain specific management measures; based on the key factors and specific management measures, the evaluation criteria of the specific management measures are defined, and corresponding weight values ​​are assigned to each evaluation criterion to obtain the evaluation criterion weight configuration; using the evaluation criterion weight configuration, combined with the data provided by the short-term ecological change trend prediction model, a multi-criteria decision analysis method is applied to conduct a comprehensive quantitative evaluation of the effects of the specific management measures to generate preliminary quantitative evaluation results; based on the preliminary quantitative evaluation results, sensitivity analysis and robustness testing are conducted to generate quantitative evaluation results, taking into account the constraints and uncertainty factors that may be encountered in actual operations.

[0046] In this embodiment, based on the short-term ecological change trend prediction model, key factors affecting the ecological health of the garden, such as meteorological conditions, soil conditions, plant growth status and visitor behavior, are identified to obtain specific management measures; based on these key factors and specific management measures, the evaluation criteria for each measure are defined (such as irrigation frequency, fertilizer application amount, pest and disease control effect and effectiveness of visitor guidance strategy), and corresponding weight values ​​are assigned to each evaluation criterion to form an evaluation criterion weight configuration; using the evaluation criterion weight configuration, combined with the data provided by the short-term ecological change trend prediction model, a multi-criteria decision analysis method is applied to conduct a comprehensive quantitative evaluation of the effects of specific management measures to generate preliminary quantitative evaluation results; based on the preliminary quantitative evaluation results, sensitivity analysis and robustness testing are conducted taking into account the constraints and uncertainty factors that may be encountered in actual operations, and finally a comprehensive quantitative evaluation result is generated.

[0047] In the embodiment of the present application, first, the system identifies the key factors affecting the ecological health of the garden based on the short-term ecological change trend prediction model, and determines the specific management measures; secondly, the system defines the evaluation criteria according to the key factors and specific management measures, and assigns weight values ​​to each criterion to construct the evaluation criteria weight configuration; thirdly, the system uses the evaluation criteria weight configuration and the data of the short-term ecological change trend prediction model, applies the multi-criteria decision analysis method, conducts a comprehensive quantitative evaluation of the effects of specific management measures, and generates preliminary quantitative evaluation results; finally, the system conducts sensitivity analysis and robustness testing on the preliminary quantitative evaluation results, takes into account the constraints and uncertainty factors in actual operations, ensures the reliability and applicability of the evaluation results, and generates the final quantitative evaluation results.

[0048] Here is a specific example: Suppose an urban park has introduced an advanced intelligent monitoring system to collect various data such as temperature, humidity, soil moisture, vegetation growth and tourist flow in real time. First, based on the short-term ecological change trend prediction model, the system identifies several key factors that affect the ecological health of the park, including drought risk, soil nutrient loss and peak tourist flow period; secondly, the system defines specific management measures based on these key factors, such as adjusting irrigation frequency, increasing the application of organic fertilizers, strengthening pest and disease monitoring and optimizing tourist guidance routes, and sets evaluation criteria for each measure (such as the rationality of irrigation frequency, scientific fertilizer application, effectiveness of pest and disease control and tourist satisfaction) and corresponding weight values; thirdly, the system uses these evaluation criteria weight configurations, combined with the data provided by the short-term ecological change trend prediction model, and applies the multi-criteria decision analysis method to conduct a comprehensive quantitative evaluation of the effects of specific management measures and obtain preliminary quantitative evaluation results; finally, the system conducts sensitivity analysis and robustness testing on the preliminary quantitative evaluation results, taking into account uncertain factors such as water resource limitations, insufficient fertilizer supply and tourist flow fluctuations that may be encountered in actual operations to ensure the reliability of the evaluation results. Through the above steps, the system generated a detailed quantitative assessment report, recommending the use of smart drip irrigation systems to improve water resource utilization during droughts, increasing the application of organic fertilizers in areas with insufficient soil nutrients to promote healthy plant growth, and optimizing guided routes during peak tourist seasons to enhance the tourist experience.

[0049] In summary, this series of steps ensures the comprehensiveness and accuracy of the evaluation of specific management measures, provides reliable quantitative evaluation results, and supports scientific and reasonable management decisions.

[0050] In order to solve the problem of evaluating the effects of specific management measures under different environmental conditions, in some embodiments, based on the quantitative evaluation results, scenario simulation technology is used to create virtual scenarios under different specific management measures, test and compare the performance of these specific management measures under various conditions, and obtain specific management measure recommendations, including: Based on the quantitative evaluation results, representative specific management measures and the weight configuration of the evaluation criteria are selected, and scenario construction processing is performed on the specific management measures to obtain a plurality of representative specific management measures; using the plurality of representative specific management measures, combined with the actual environmental parameters and historical data in the garden ecological portrait, scenario simulation technology is applied to create different virtual scenes, and each virtual scene corresponds to an implementation plan of the management measures to obtain a plurality of sets of virtual scene sets; according to the plurality of sets of virtual scene sets, different environmental conditions and operating parameters are set, and simulation tests are performed on the performance of each specific management measure under different conditions, and test data of the simulation test is collected to obtain simulation test results; based on the simulation test results, the effect differences of different specific management measures under various environmental conditions are compared and analyzed, the optimal or suboptimal specific management measures are identified, and specific management measures recommendations are generated.

[0051] In this embodiment, based on the quantitative evaluation results, representative specific management measures and evaluation criteria weight configurations are selected, and scenario construction processing is performed on these management measures to obtain multiple representative specific management measures; using these representative specific management measures, combined with the actual environmental parameters and historical data in the garden ecological portrait, scenario simulation technology is applied to create different virtual scenes, each virtual scene corresponds to a specific management measure implementation plan, forming multiple groups of virtual scene sets; according to these virtual scene sets, different environmental conditions and operating parameters are set, and the performance of each specific management measure under different conditions is simulated and tested, and the test data of the simulation test is collected to obtain the simulation test results; based on the simulation test results, the effect differences of different specific management measures under various environmental conditions are compared and analyzed, the optimal or suboptimal specific management measure schemes are identified, and specific management measure recommendations are generated.

[0052] In the embodiment of the present application, first, the system selects representative specific management measures and their evaluation criteria weight configurations based on the quantitative evaluation results, and performs scenario construction processing to ensure that all key management strategies are covered; secondly, the system uses the selected representative specific management measures, combined with the actual environmental parameters (such as weather, soil, vegetation, etc.) and historical data in the garden ecological portrait, and applies scenario simulation technology to create multiple different virtual scenes, each virtual scene corresponds to a specific management measure implementation plan, forming multiple groups of virtual scene sets; thirdly, the system sets different environmental conditions (such as drought, floods, etc.) and operating parameters (such as irrigation frequency, fertilizer application amount, etc.) according to the multiple groups of virtual scene sets, simulates the performance of each specific management measure under different conditions, collects test data of the simulation test, and obtains simulation test results; finally, based on the simulation test results, the system compares and analyzes the differences in the effects of different specific management measures under various environmental conditions, identifies the optimal or suboptimal specific management measures, and finally generates specific management measures recommendations.

[0053] Here is a specific example: Suppose a city park introduces an advanced intelligent monitoring system to collect real-time data such as temperature, humidity, soil moisture, vegetation growth, and visitor flow. First, based on the quantitative evaluation results, the system selected representative specific management measures, such as intelligent drip irrigation system, organic fertilizer application, pest and disease control, and optimized tourist guidance routes, and set evaluation standard weight configurations for each measure; second, using these representative specific management measures, combined with the actual environmental parameters (such as soil moisture, temperature changes) and historical data (such as drought and flood records in the past few years) in the park's ecological portrait, the system applied scenario simulation technology to create multiple different virtual scenes. Each virtual scene corresponds to a specific management measure implementation plan, forming multiple sets of virtual scene sets; third, based on these virtual scene sets, the system set different environmental conditions (such as extreme drought, mild floods) and operating parameters (such as irrigation frequency, fertilizer application amount), simulated the performance of each specific management measure under different conditions, collected test data for the simulation test, and obtained detailed simulation test results; finally, based on the simulation test results, the system compared and analyzed the differences in the effects of different specific management measures under various environmental conditions, and identified the optimal or suboptimal specific management measures. For example, the intelligent drip irrigation system performed well under drought conditions, while the optimization of tourist guidance routes significantly improved tourist satisfaction. Through the above steps, the system generates a detailed recommendation for specific management measures to guide managers to take the most effective management measures under different environmental conditions.

[0054] In summary, this series of steps ensures the accuracy and reliability of the effectiveness evaluation of specific management measures under different environmental conditions, provides scientific and reasonable support for management decision-making, and achieves effective allocation of resources and continuous optimization of tourist experience.

[0055] The present application takes into account that in the prior art, since ecological change prediction models usually rely on static data and simple models, they lack comprehensive consideration of dynamic environmental factors, resulting in insufficient prediction accuracy. In addition, traditional methods fail to fully combine multi-source heterogeneous data for comprehensive evaluation, ignore the impact of periodic and seasonal changes, and find it difficult to accurately capture the fluctuating characteristics of the ecosystem. Therefore, the invention embodiment proposes this optional solution, which aims to achieve probabilistic prediction processing of ecological change trends that may occur in the short term by introducing a Bayesian network algorithm and an ecosystem fitness score, and generate a short-term ecological change trend prediction model to solve the problems of low prediction accuracy and poor adaptability in the prior art.

[0056] Optionally, based on the current ecological health status assessment result, a Bayesian network algorithm is applied to perform probability prediction processing on the ecological change trend that may occur in the short term to obtain a short-term ecological change trend prediction model, including: A comprehensive assessment of the current ecological health status is conducted. By applying the Bayesian network algorithm, a probabilistic prediction process is performed based on the current ecological health status assessment results and the actual implementation of specific management measures. Taking into account the periodic and seasonal changes in the garden's ecosystem, a periodic function is introduced to capture the impact of these fluctuations and generate a probability for predicting the occurrence of a specific ecological change in the next time period. The probability of a specific ecological change in the next time period is Calculated by the following formula: ; in, Indicates that given the current ecological health status and impact factor comprehensive score Under the condition of the probability of a particular ecological change occurring within a region; It is the result of the current ecological health status assessment; It is a comprehensive score of impact factors calculated by multi-criteria decision analysis method; Indicates the current ecological health status The weight of impact on future changes; Indicates the comprehensive score of impact factor The weight of impact on future changes; Indicates The weight of the evaluation criteria for each specific management measure; Indicates The actual implementation score of each specific management measure; It represents the overall impact coefficient of the implementation of management measures on ecological changes; Represents the model bias term, which adjusts the base level of predicted probability; Indicates the number of specific management measures; represents the seasonal fluctuation coefficient, which is used to capture the impact of cyclical changes; Indicates the current time point; Indicates the length of the cycle, usually one year or half a year; It means that the output value is guaranteed to be between 0 and 1, indicating the probability range; Based on the probability of ecological change, the impact weight of the ecosystem fitness score, the impact coefficient of the actual implementation effect of resource allocation measures on fitness, the seasonal fluctuation impact coefficient and the cycle frequency are introduced, and multi-source data analysis and machine learning algorithms are used to generate the ecosystem fitness score. ; The ecosystem fitness score is calculated using the following formula: : ; in, Indicates time period Ecosystem fitness scores within Represents the probability of ecological change The weight of the impact on fitness; Indicates the comprehensive score of impact factor The weight of the impact on fitness; Indicates The influence coefficient of the actual implementation effect of each resource allocation measure on fitness; Indicates The actual implementation effect score of a specific resource allocation measure; Indicates The utility rating of each resource allocation measure; Indicates the number of specific resource allocation measures; Represents the model bias term, which adjusts the base level of fitness score; It represents the seasonal fluctuation impact coefficient, which is used to capture the impact of cyclical changes; represents the periodic frequency; Based on the ecosystem fitness score, iterative optimization processing and simulation analysis are used to combine the ecosystem fitness score with the existing resource allocation plan and tour experience optimization plan, and the optimal solution is found through the particle swarm optimization algorithm. Through continuous iterative optimization and continuous learning mechanism, a short-term ecological change trend prediction model is formed.

[0057] This formula aims to improve the accuracy of short-term ecological change trend prediction. The present invention introduces two key formulas: The first formula is used to calculate the probability of a specific ecological change occurring in the next time period. , the second formula is used to calculate the ecosystem fitness score The purpose of these formulas is to provide more accurate predictions based on the current state of ecological health and the implementation of specific management measures, combined with the impact of cyclical and seasonal changes, and to ensure the continuous optimization of resource allocation and visitor experience through iterative optimization and simulation analysis.

[0058] The following is a brief introduction to the design reasons of each sub-item of the formula: ; :This sub-item considers the impact of the current ecological health status on future ecological change trends. A weight , the model can quantify the potential impact of current ecosystem conditions on future changes, ensuring that the prediction results fully reflect the existing ecological conditions.

[0059] :This sub-item introduces the comprehensive score of impact factor , combined with multi-criteria decision analysis methods to assess the comprehensive impact of various environmental and socio-economic factors on the ecosystem. , the model can more comprehensively capture the joint effects of multiple factors on future ecological changes.

[0060] :This sub-item considers the actual implementation of specific management measures and their overall impact on ecological change. By assigning evaluation criteria weights to each management measure And score based on actual execution , then multiplied by the overall impact coefficient , the model can accurately assess the effectiveness of management measures and their specific contributions to future ecological changes.

[0061] :This sub-item introduces periodic functions to capture the periodic and seasonal changes in garden ecosystems. By using a sine function and setting the period length The model can simulate natural fluctuations within a year or half a year and improve the accuracy of predictions on short-term ecological change trends.

[0062] The following is a brief introduction to how to obtain the parameters of the formula: and Determined through historical data analysis and expert evaluation; From real-time monitoring data and historical records; Calculated by multi-criteria decision analysis method; Determined based on an evaluation of the historical effectiveness of management measures; and Obtained through specific management measures evaluation and actual implementation records; is a constant obtained by adjusting the model training; and Determined based on historical data of seasonal and cyclical changes; The following is a brief introduction to the design reasons of each sub-item of the formula: ; :This sub-item takes into account the probability of short-term ecological changes Impact on ecosystem fitness. By assigning weights , the model can quantify the specific impact of possible changes in the short term on the future fitness of the ecosystem, ensuring that the fitness score is more scientific and reasonable.

[0063] :This sub-item once again introduces the comprehensive score of impact factor , but this time to assess its impact on ecosystem fitness. , the model can comprehensively consider the long-term impact of multiple factors on ecosystem fitness and provide more comprehensive evaluation results.

[0064] :This sub-item considers the actual implementation effect of resource allocation measures and their impact on ecosystem fitness. and utility rating Perform a weighted square sum and multiply by the influence coefficient , the model can comprehensively evaluate the effects of resource allocation measures and their contribution to ecosystem fitness.

[0065] :This sub-item introduces the cosine function to capture the impact of periodic and seasonal changes on ecosystem fitness. , the model can simulate natural fluctuations in different time periods, ensuring that the fitness score fully takes into account seasonal and cyclical changes, improving the accuracy and reliability of the prediction.

[0066] The following is a brief introduction to how to obtain the parameters of the formula: and Determined through historical data analysis and expert evaluation; Calculated by multi-criteria decision analysis method; Determined based on an evaluation of the historical effectiveness of resource allocation measures; and Obtained through specific resource allocation measures assessment and actual implementation records; is a constant obtained by adjusting the model training; and Determined based on historical data of seasonal and cyclical changes; Suppose a city park has introduced an advanced intelligent management system and formulated a resource allocation plan and a tour experience optimization plan. First, the park evaluates the current ecological health status. (Good) and impact factor comprehensive score (high), and the actual implementation of specific management measures (e.g. irrigation measures scored , the fertilization measure score is ), the probability of a specific ecological change occurring in the next time period is calculated using the following formula ; ; ; Next, the park based on the above ecological change probability , the ecosystem fitness score is calculated using the following formula : ; Through the above steps, the park has achieved accurate prediction of short-term ecological change trends and calculation of fitness scores, ensuring continuous optimization of resource allocation and visitor experience. Assuming the interval value is set to 0.75, This is greater than the set threshold, indicating that the park’s ecological health and visitor experience are both at an excellent level, further demonstrating the effectiveness of the program.

[0067] In summary, this series of steps not only improves the accuracy of short-term ecological change trend predictions, but also continuously optimizes resource allocation and tourist guidance strategies through a continuous learning mechanism, thereby achieving effective resource allocation and continuous improvement of tourist experience.

[0068] In order to solve the problem of dynamic adjustment of maintenance operations and tourist guidance strategies, in some embodiments, the maintenance operation time and maintenance work intensity as well as the tourist guidance strategy in the garden are automatically adjusted according to the ecological change trend forecast report in step 103, and the particle swarm optimization algorithm is used to find the optimal solution for automatic adjustment, and based on the garden ecological health and tourist experience analysis in the garden ecological portrait, a resource allocation plan and a tour experience optimization plan are generated, including: The ecological change trend forecast report is used to analyze the current and short-term ecological conditions and potential changes in the future, identify the types of maintenance operations and tourist guidance strategies that need to be adjusted, and obtain an adjustment demand list; based on the adjustment demand list, combined with the historical maintenance operation time and maintenance work intensity, the particle swarm optimization algorithm is applied to automatically adjust the maintenance operation time, maintenance work intensity and tourist guidance strategy on the premise of meeting resource constraints, find the optimal solution for automatic adjustment, and generate a preliminary adjustment plan; based on the preliminary adjustment plan, the garden ecological health data in the garden ecological portrait is used to evaluate the effect of the adjusted maintenance operations to obtain an ecological protection evaluation result; based on the tourist experience analysis in the garden ecological portrait, the impact of the adjusted tourist guidance strategy on tourist satisfaction is evaluated to obtain a tourist experience evaluation result; the ecological protection evaluation result and the tourist experience evaluation result are integrated to further optimize the preliminary adjustment plan to generate a resource allocation plan and a tour experience optimization plan.

[0069] In this embodiment, the data in the ecological change trend forecast report is used to analyze the current and short-term ecological conditions and potential changes in the future, identify the types of maintenance operations (such as irrigation, fertilization, pest control) and tourist guidance strategies (such as route optimization, activity arrangement) that need to be adjusted, and obtain a list of adjustment requirements; based on these adjustment requirements, combined with historical maintenance operation time and work intensity data, the particle swarm optimization algorithm is applied to automatically adjust the maintenance operation time and intensity and tourist guidance strategy under the premise of meeting resource constraints, find the optimal solution for automatic adjustment, and generate a preliminary adjustment plan; using the garden ecological health data in the garden ecological portrait, evaluate the effect of the adjusted maintenance operations and obtain the ecological protection evaluation results; at the same time, based on the tourist experience analysis in the garden ecological portrait, evaluate the impact of the adjusted tourist guidance strategy on tourist satisfaction and obtain the tourist experience evaluation results; integrate the ecological protection evaluation results and the tourist experience evaluation results, further optimize the preliminary adjustment plan, and finally generate a resource allocation plan and a tour experience optimization plan.

[0070] In the embodiment of the present application, first, the system uses the ecological change trend forecast report to analyze the current and future short-term ecological conditions and potential changes, identify the specific maintenance operation types and tourist guidance strategies that need to be adjusted, and form an adjustment demand list; secondly, the system combines the historical maintenance operation time and intensity data, applies the particle swarm optimization algorithm, and finds the optimal solution for automatic adjustment under resource constraints to generate a preliminary adjustment plan; thirdly, the system evaluates the effect of the adjusted maintenance operations based on the ecological health data in the garden ecological portrait, and obtains the ecological protection evaluation results; at the same time, the system evaluates the impact of the adjusted tourist guidance strategy on tourist satisfaction based on the tourist experience analysis, and obtains the tourist experience evaluation results; finally, the system integrates the ecological protection evaluation results and the tourist experience evaluation results, further optimizes the preliminary adjustment plan, and generates the final resource allocation plan and tour experience optimization plan.

[0071] Here is a specific example: Suppose a city park introduces an advanced intelligent monitoring system to collect real-time data such as temperature, humidity, soil moisture, vegetation growth, and visitor flow. First, the system uses the ecological change trend forecast report to analyze and find that there will be a risk of heavy rainfall in the next week, which may cause waterlogging and plant root hypoxia in some areas. It identifies the maintenance work needs that require early drainage and enhanced ventilation, as well as the need for tourist guidance strategies that increase safety warnings, and forms a list of adjustment needs; secondly, the system combines the maintenance work time and intensity data of the past few years, applies the particle swarm optimization algorithm, takes into account the number of existing drainage equipment and staff scheduling, finds the best drainage path and schedule, adjusts the tourist guidance route, and generates a preliminary adjustment plan; thirdly, based on the soil moisture and plant health data in the garden ecological portrait, the system evaluates the positive impact of the adjusted drainage measures on plant health and obtains the ecological protection evaluation results; at the same time, based on the tourist experience analysis, the system evaluates the role of the newly added safety warning signs in enhancing tourists' sense of security and obtains the tourist experience evaluation results; finally, the system integrates the ecological protection evaluation results and the tourist experience evaluation results, further optimizes the preliminary adjustment plan, generates the final resource allocation plan and tour experience optimization plan, and ensures the garden safety and tourist convenience during the rainy season. Through the above steps, the system realizes the intelligent dynamic adjustment of maintenance operations and tourist guidance strategies, ensuring the effective allocation of resources and continuous optimization of tourist experience.

[0072] In summary, this series of steps ensures the scientific and reasonable adjustment of maintenance operations and tourist guidance strategies, provides a reliable resource allocation plan and tour experience optimization plan, and meets the needs of efficient resource management and high-quality tourist experience.

[0073] In order to solve the problem of evaluating the impact of the adjusted tourist guidance strategy on tourist satisfaction, in some embodiments, the tourist experience analysis based on the garden ecological portrait is used to evaluate the impact of the adjusted tourist guidance strategy on tourist satisfaction, and obtain the tourist experience evaluation result, including: By using the tourist experience data in the garden ecological portrait, a variety of indicators related to tourist satisfaction are extracted to form a tourist experience index system; according to the tourist experience index system, combined with the adjusted tourist guidance strategy, the tourist flow of each scenic spot and area in the garden is predicted and processed, the tourist flow in different time periods is simulated, and the tourist flow simulation results are generated; based on the tourist flow simulation results, the user experience scoring model is applied, and the factors of tourist stay time, queue waiting time, and congestion level are comprehensively considered to calculate the tourist satisfaction score of each scenic spot or area to obtain a preliminary tourist satisfaction assessment; using the preliminary tourist satisfaction assessment, combined with historical tourist feedback data and social media evaluation information, multi-source data analysis is carried out to calibrate and optimize the tourist satisfaction score to generate a tourist experience assessment result.

[0074] In this embodiment, the tourist experience data (such as tourist flow, dwell time, waiting time in queue, degree of crowding, etc.) in the garden ecological portrait is used to extract various indicators related to tourist satisfaction and form a tourist experience indicator system; based on these indicator systems and in combination with the adjusted tourist guidance strategy, the tourist flow of each scenic spot and area in the garden is predicted, the tourist flow in different time periods is simulated, and the tourist flow simulation results are generated; based on the tourist flow simulation results, the user experience scoring model is applied, and the tourist residence time, waiting time in queue, degree of crowding and other factors are comprehensively considered to calculate the tourist satisfaction score of each scenic spot or area, and obtain a preliminary tourist satisfaction assessment; the preliminary tourist satisfaction assessment is used to combine historical tourist feedback data and social media evaluation information to conduct multi-source data analysis, calibrate and optimize the tourist satisfaction score, and finally generate the tourist experience assessment results.

[0075] In the embodiment of the present application, firstly, the system utilizes the tourist experience data in the garden ecological portrait to extract a variety of indicators related to tourist satisfaction, and forms a comprehensive tourist experience indicator system; secondly, the system predicts the tourist flow of each scenic spot and area based on the indicator system and the adjusted tourist guidance strategy, and simulates the tourist flow in different time periods to generate detailed tourist flow simulation results; thirdly, based on the tourist flow simulation results, the system applies the user experience scoring model, comprehensively considers factors such as tourist stay time, waiting time in queues, and degree of congestion, calculates the tourist satisfaction score for each scenic spot or area, and obtains a preliminary tourist satisfaction assessment; finally, the system uses the preliminary tourist satisfaction assessment, combined with historical tourist feedback data and social media evaluation information, to conduct multi-source data analysis, calibrate and optimize the tourist satisfaction score, and generate the final tourist experience evaluation result.

[0076] Here is a specific example: Suppose a city park has introduced an advanced intelligent monitoring system to collect various data such as temperature, humidity, soil moisture, vegetation growth and tourist flow in real time. First, the system uses the tourist experience data in the garden ecological portrait to extract various indicators related to tourist satisfaction, such as tourist stay time, waiting time in queues, and crowding, and forms a tourist experience index system; secondly, based on this index system, combined with the newly adjusted tourist guidance strategy (such as the newly added safety warning signs and optimized tour routes), the system predicts the tourist flow of each scenic spot and area in the park, and simulates the tourist flow in different time periods, generating detailed tourist flow simulation results; thirdly, based on these simulation results, the system applies the user experience scoring model, comprehensively considers factors such as tourist stay time, waiting time in queues and crowding, calculates the tourist satisfaction score of each scenic spot or area, and obtains a preliminary tourist satisfaction evaluation; finally, the system uses the preliminary evaluation results, combined with historical tourist feedback data (such as complaint records, satisfaction surveys) and social media evaluation information (such as comments on Weibo and WeChat) in the past few years, conducts multi-source data analysis, corrects and optimizes the tourist satisfaction score, and generates the final tourist experience evaluation results. Through the above steps, the system accurately evaluates the impact of the adjusted tourist guidance strategy on tourist satisfaction, provides scientific and reasonable optimization suggestions, and ensures the continuous improvement of tourist experience.

[0077] In summary, this series of steps ensures an accurate assessment of the impact of adjusted tourist guidance strategies on tourist satisfaction, provides reliable tourist experience evaluation results, supports scientific and reasonable management decisions, and achieves continuous optimization of tourist experience.

[0078] This application takes into account that in the prior art, due to the lack of intelligent and adaptive adjustment mechanisms, the optimization of maintenance operation time and intensity and tourist guidance strategies often relies on static data and empirical judgments, and it is difficult to respond to environmental changes and resource constraints in real time. This leads to problems such as unreasonable resource allocation, inefficient management, and poor tourist experience. Therefore, the embodiment of the invention proposes this optional solution, which aims to dynamically adjust the maintenance operation time and intensity and tourist guidance strategies through the particle swarm optimization algorithm (PSO) combined with multi-source data analysis, and find the optimal solution for automatic adjustment to solve the problems of inflexible adjustment mechanism and poor optimization effect in the prior art.

[0079] In summary, this series of steps ensures the continuous improvement of resource allocation and tour experience optimization plans, provides reliable management system configuration, supports scientific and reasonable management decisions, and realizes the effective allocation of resources and continuous optimization of tourist experience.

[0080] Optionally, according to the adjustment demand list, combined with the existing maintenance operation schedule and intensity settings, a particle swarm optimization algorithm is applied to automatically adjust the maintenance operation time and maintenance operation intensity and tourist guidance strategy under the premise of meeting resource constraints, find the optimal solution for automatic adjustment, and generate a preliminary adjustment plan, including: According to the adjustment demand list, combined with the existing maintenance operation schedule and intensity settings, data analysis and particle swarm optimization algorithm processing are used to evaluate key influencing factors and set initial conditions, generate initial positions and velocities of particles through multi-source data analysis, and calculate new positions by combining historical optimal positions and global optimal positions; The new position is calculated using the following formula : ; in, Indicates Particles in time Current location; is the velocity at the next time step; and They are the current inertia weight and the minimum inertia weight respectively; It is a particle The best position in history; It is a social learning factor; is a random number; is the global optimal position; Based on the new position, inertia weight, cognitive and social learning factor parameters are introduced, and speed adjustment analysis is applied to re-evaluate the learning effect of the particle using the updated position information and the best position data, simulate the uncertainty in nature, and calculate the new speed ; The new speed is calculated using the following formula : ; in, Indicates Particles in time speed; is the inertia weight; It is a cognitive learning factor; is a random number; is the amount of resources used; It is a resource constraint; is the resource adjustment factor; is another random number; Based on the new speed, iterative optimization and convergence analysis are performed, the termination condition or the maximum number of iterations is checked, the particle information of the new position and the new speed is sorted, the optimal solution of the particle information is selected, the impact of the optimal solution on maintenance operations and tourist guidance strategies is analyzed, and a preliminary adjustment plan is generated.

[0081] The formula aims to achieve intelligent optimization of maintenance operation time and intensity and tourist guidance strategy. The present invention introduces two key formulas: the first formula is used to calculate the new location , the second formula is used to calculate the new speed The purpose of these formulas is to find the optimal solution and generate a preliminary adjustment plan by considering the existing maintenance operation schedule and intensity settings, combining the historical optimal position and the global optimal position, and ensuring that the adjustment process is consistent with the actual resource constraints and simulates the uncertainty of nature through inertia weights, cognitive and social learning factor parameters.

[0082] The following is a brief introduction to the design reasons of each sub-item of the formula: ; : This sub-item considers the relationship between the particle's speed change and inertia weight, as well as the particle's tendency to move toward its historical best position. By combining speed adjustment with inertia weight and introducing the square of the distance from the particle to its historical best position, the model can dynamically adjust the particle's moving direction and speed, ensuring that the particle gradually approaches the optimal solution while effectively exploring the search space.

[0083] :This sub-item introduces social learning factors and social randomness to simulate the process of particles learning to the global optimal position. By combining the cubic term of the distance between the current position of the particle and the global optimal position with the social learning factor and random numbers, the model can promote information sharing between particles, enhance the group's collaborative ability, and thus accelerate convergence to the global optimal solution.

[0084] The following is a brief introduction to how to obtain the parameters of the formula: From existing maintenance work schedule and intensity settings; Calculated by formula 2; and Dynamically adjust according to the number of iterations; Get from the particle's history; Determined through experimentation or expert evaluation; is a randomly generated value; Select from the best positions of all particles; The following is a brief introduction to the design reasons of each sub-item of the formula: ; : This sub-item takes into account the inertia of the particle and retains a portion of the current velocity to maintain the particle's inertia. By giving the particle an inertia weight, the model can maintain the particle's original motion trend while allowing it to adjust according to new information, ensuring that the search process is both stable and flexible.

[0085] :This sub-item introduces cognitive learning factors and cognitive randomness to simulate the process of particles learning from their own historical best positions. By combining the square root of the distance between the particle's current position and its historical best position with the cognitive learning factor and random numbers, the model can enhance the particle's learning of its own experience, allowing it to more effectively use existing knowledge during the exploration process and improve the optimization effect.

[0086] This sub-item takes into account the impact of resource constraints on particle speed to ensure that the optimization process meets the actual resource constraints. By introducing resource adjustment factors and the proportional cube root of resource usage, the model can dynamically adjust the particle speed during the optimization process to avoid exceeding resource constraints and ensure the feasibility and practicality of the solution.

[0087] The following is a brief introduction to how to obtain the parameters of the formula: Dynamically adjust according to the number of iterations; The velocity value from the previous step; Determined through experimentation or expert evaluation; is a randomly generated value; Get from the particle's history; Set according to resource constraints; is a randomly generated value; From system settings; From real-time monitoring data; Suppose a city park has introduced an advanced intelligent management system, formulated maintenance operation time and intensity settings, and applied the particle swarm optimization algorithm to automatically adjust the adjustment process based on the adjustment demand list and the existing maintenance operation schedule and intensity settings. First, the system uses data analysis and particle swarm optimization algorithm to process the adjustment demand list, evaluate key influencing factors and set initial conditions, generate the initial position and speed of particles through multi-source data analysis, and calculate the new position by combining the historical best position and the global best position. : ; Based on the above new position, the system introduces inertia weight, cognitive and social learning factor parameters, and applies speed adjustment analysis. It uses the updated position information and optimal position data to re-evaluate the learning effect of the particles, simulate the uncertainty of nature, and calculate the new speed. : ; Through the above steps, the system realizes the intelligent optimization of maintenance operation time and intensity and tourist guidance strategy, ensuring that the adjustment process not only meets the actual resource constraints but also simulates the uncertainty of nature. It is exactly equal to the set threshold, so it shows that the optimal solution has been found, which further proves the effectiveness of the scheme.

[0088] In summary, this series of steps not only improves the optimization effect of maintenance operation time and intensity and tourist guidance strategy, but also continuously improves resource allocation through a continuous learning mechanism, thereby achieving effective resource allocation and continuous improvement of tourist experience.

[0089] In order to solve the problem of continuous improvement of resource allocation and tour experience optimization plan, in some embodiments, the step 104 establishes a continuous learning mechanism based on the resource allocation plan and tour experience optimization plan, implements a regular review process, and generates a management system configuration, including: Execute the resource allocation plan and tour experience optimization plan, track and monitor the actual implementation effect, collect multi-source data on maintenance operation execution, resource utilization efficiency and tourist feedback, and obtain implementation effect monitoring data; based on the implementation effect monitoring data, combined with the dynamic change information in the garden ecological portrait, apply the intelligent decision-making algorithm to analyze the deviation between the actual implementation effect and the expected implementation effect, identify the areas where the implementation effect needs to be improved, and generate a deviation analysis report; based on the deviation analysis report, design and implement a regular review process to review the effectiveness of the resource allocation plan and the tour experience optimization plan, and obtain regular review results; use the regular review results to update the parameter settings in the intelligent decision-making algorithm, adjust the resource allocation and tourist guidance strategies, introduce the impact assessment of socio-economic factors, and generate a management system configuration.

[0090] In this embodiment, the resource allocation plan and the tour experience optimization plan are executed, and the actual implementation effect is tracked and monitored, and multi-source data such as the execution status of maintenance operations, resource utilization efficiency, and tourist feedback are collected to form implementation effect monitoring data; based on these implementation effect monitoring data, combined with the dynamic change information in the garden ecological portrait, the intelligent decision-making algorithm is used to analyze the deviation between the actual implementation effect and the expected implementation effect, identify the areas that need improvement, and generate a deviation analysis report; based on the deviation analysis report, a regular review process is designed and implemented to review the effectiveness of the resource allocation plan and the tour experience optimization plan, and obtain regular review results; using the regular review results, the parameter settings in the intelligent decision-making algorithm are updated, the resource allocation and tourist guidance strategies are adjusted, and the impact assessment of socio-economic factors is introduced to finally generate a management system configuration.

[0091] In the embodiment of the present application, first, the system executes the resource allocation plan and the tour experience optimization plan, and at the same time tracks and monitors the actual implementation effect, collects multi-source data such as the execution of maintenance operations, resource utilization efficiency and tourist feedback, and forms comprehensive execution effect monitoring data; secondly, the system applies the intelligent decision-making algorithm to analyze the deviation between the actual execution effect and the expected execution effect based on the execution effect monitoring data and the dynamic change information in the garden ecological portrait, identifies the areas that need improvement, and generates a detailed deviation analysis report; thirdly, based on the deviation analysis report, the system designs and implements a regular review process to review the effectiveness of the resource allocation plan and the tour experience optimization plan to ensure that they adapt to the latest environmental changes and obtain regular review results; finally, the system uses the regular review results to update the parameter settings in the intelligent decision-making algorithm, adjust the resource allocation and tourist guidance strategies, and introduce the impact assessment of socio-economic factors (such as cost-benefit analysis) to generate a new management system configuration to ensure the system's adaptability and advancement.

[0092] Here is a specific example: Suppose a city park introduces an advanced intelligent management system and develops a resource allocation plan and a tour experience optimization plan. First, the park implemented irrigation, fertilization and pest control measures according to the resource allocation plan, and adjusted the tourist guidance route according to the tour experience optimization plan. At the same time, multi-source data such as maintenance operation execution, resource utilization efficiency and tourist feedback were collected through sensors and online platforms to form comprehensive execution effect monitoring data; secondly, based on these execution effect monitoring data, combined with the dynamic change information in the garden ecological portrait (such as soil moisture, plant growth status), the system applied intelligent decision-making algorithms to analyze the deviation between the actual execution effect and the expected execution effect, and found that the excessive irrigation frequency in some areas led to water waste, and generated a detailed deviation analysis report; thirdly, based on the deviation analysis report, the system designed and implemented a regular review process once a quarter to review the effectiveness of the resource allocation plan and the tour experience optimization plan to ensure that they adapt to the latest environmental changes, and obtained regular review results, such as finding that tourist satisfaction in some areas was lower than expected; finally, the system used the regular review results to update the parameter settings in the intelligent decision-making algorithm, adjust the resource allocation (such as reducing the irrigation frequency in specific areas) and tourist guidance strategies (such as adding rest areas), and introduced the impact assessment of socio-economic factors (such as cost-benefit analysis) to generate a new management system configuration to ensure the system's adaptability and advancement. Through the above steps, the park has achieved continuous improvement in resource allocation and tour experience optimization plans, thereby improving management efficiency and visitor satisfaction.

[0093] Figure 2 A schematic diagram of the structure of a smart garden management system based on big data technology is provided for the embodiment of the present application. Figure 2 As shown, the device comprises: The acquisition module 21 is used to collect multi-source heterogeneous data in the garden in real time through the environmental perception network, and integrate and compare the multi-source heterogeneous data in combination with the geographic information system and time series analysis to obtain a garden ecological portrait; Evaluation module 22, used to evaluate the current ecological health status of the garden by using the garden ecological portrait, and to predict the ecological change trend that may occur in the short term by applying the Bayesian network algorithm, and to generate an ecological change trend prediction report by introducing a multi-criteria decision analysis method combined with scenario simulation technology; The adjustment module 23 is used to automatically adjust the maintenance operation time and maintenance work intensity and the tourist guidance strategy in the garden according to the ecological change trend prediction report, use the particle swarm optimization algorithm to find the optimal solution for automatic adjustment, and generate a resource allocation plan and a tour experience optimization plan based on the garden ecological health and tourist experience analysis in the garden ecological portrait; A module 24 is established to establish a continuous learning mechanism, implement a regular review process, and generate a management system configuration based on the resource allocation plan and the tour experience optimization plan.

[0094] Figure 2 The smart garden management system based on big data technology can execute Figure 1 The implementation principle and technical effect of the smart garden management method based on big data technology described in the embodiment shown will not be repeated. The specific way in which each module and unit performs operations in the smart garden management system based on big data technology in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0095] In one possible design, Figure 2 A smart garden management system based on big data technology in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0096] The processing component 32 is used to: collect multi-source heterogeneous data in the garden in real time through the environmental perception network, integrate and compare the multi-source heterogeneous data in combination with the geographic information system and time series analysis to obtain a garden ecological portrait; use the garden ecological portrait to evaluate the current garden ecological health status, and apply the Bayesian network algorithm to predict the ecological change trend that may occur in the short term, introduce a multi-criteria decision analysis method combined with scenario simulation technology to generate an ecological change trend forecast report; according to the ecological change trend forecast report, automatically adjust the maintenance operation time and maintenance work intensity as well as the tourist guidance strategy in the garden, use the particle swarm optimization algorithm to find the optimal solution for automatic adjustment, and generate a resource allocation plan and a tour experience optimization plan based on the garden ecological health and tourist experience analysis in the garden ecological portrait; based on the resource allocation plan and the tour experience optimization plan, establish a continuous learning mechanism, implement a regular review process, and generate a management system configuration.

[0097] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method.

[0098] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a smart garden management method based on big data technology.

[0099] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0100] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A smart garden management method based on big data technology, characterized in that: include: The multi-source heterogeneous data in the garden are collected in real time through the environmental perception network, and the multi-source heterogeneous data are integrated and compared with the geographic information system and time series analysis to obtain the ecological portrait of the garden; The garden ecological portrait is used to evaluate the current ecological health status of the garden, and the Bayesian network algorithm is used to predict the ecological change trend that may occur in the short term. The multi-criteria decision analysis method is introduced in combination with the scenario simulation technology to generate an ecological change trend forecast report; According to the ecological change trend forecast report, the maintenance operation time and maintenance work intensity as well as the tourist guidance strategy in the garden are automatically adjusted, and the particle swarm optimization algorithm is used to find the optimal solution for automatic adjustment, and based on the garden ecological health and tourist experience analysis in the garden ecological portrait, a resource allocation plan and a tour experience optimization plan are generated; Based on the resource allocation plan and tour experience optimization plan, establish a continuous learning mechanism, implement a regular review process, and generate a management system configuration.

2. The method according to claim 1, characterized in that The garden ecological portrait is used to evaluate the current garden ecological health status, and the Bayesian network algorithm is used to predict the ecological change trend that may occur in the short term. The multi-criteria decision analysis method is introduced in combination with the scenario simulation technology to generate an ecological change trend forecast report, including: Using the multi-source heterogeneous data and long-term trend and seasonal pattern recognition results in the garden ecological portrait, a comprehensive assessment is conducted on the current garden ecological health status to obtain the current ecological health status assessment result; Based on the current ecological health status assessment results, a Bayesian network algorithm is used to perform probabilistic prediction processing on the ecological change trends that may occur in the short term, and a short-term ecological change trend prediction model is obtained; According to the short-term ecological change trend prediction model, the evaluation criteria weights for specific management measures such as irrigation plans, fertilization plans, pest control measures and visitor guidance strategies in the garden are set, and the effects of the specific management measures are quantitatively evaluated through a multi-criteria decision analysis method to generate quantitative evaluation results; Based on the quantitative evaluation results, using scenario simulation technology to create virtual scenarios under different specific management measures, test and compare the performance of the different specific management measures under various conditions, and obtain specific management measure recommendations; Integrate the specific management measures and short-term ecological change trend prediction model to generate an ecological change trend prediction report.

3. The method according to claim 2, characterized in that According to the short-term ecological change trend prediction model, the evaluation criteria weights for specific management measures such as irrigation plans, fertilization plans, pest control measures and visitor guidance strategies in the garden are set, and the effects of the specific management measures are quantitatively evaluated through a multi-criteria decision analysis method to generate quantitative evaluation results, including: Based on the short-term ecological change trend prediction model, key factors affecting the ecological health of the garden are identified and specific management measures are obtained; Based on the key factors and specific management measures, define the evaluation criteria of the specific management measures, and assign corresponding weight values ​​to each evaluation criterion to obtain the evaluation criterion weight configuration; Using the weight configuration of the evaluation criteria, combined with the data provided by the short-term ecological change trend prediction model, a multi-criteria decision analysis method is applied to conduct a comprehensive quantitative evaluation of the effects of the specific management measures to generate preliminary quantitative evaluation results; Based on the preliminary quantitative evaluation results, sensitivity analysis and robustness testing are carried out to generate quantitative evaluation results, taking into account the constraints and uncertainties that may be encountered in actual operations.

4. The method according to claim 2, characterized in that: Based on the quantitative evaluation results, scenario simulation technology is used to create virtual scenarios under different specific management measures, test and compare the performance of these specific management measures under various conditions, and obtain specific management measures recommendations, including: Based on the quantitative evaluation results, representative specific management measures and the weight configuration of the evaluation criteria are selected, and scenario construction processing is performed on the specific management measures to obtain multiple representative specific management measures; Using the multiple representative specific management measures, combined with actual environmental parameters and historical data in the garden ecological portrait, scenario simulation technology is applied to create different virtual scenes, and each virtual scene corresponds to a management measure implementation plan, to obtain multiple sets of virtual scene sets; According to the multiple sets of virtual scene sets, different environmental conditions and operating parameters are set, simulation tests are performed on the performance of each specific management measure under different conditions, test data of the simulation tests are collected, and simulation test results are obtained; Based on the simulation test results, the effects of different specific management measures under various environmental conditions are compared and analyzed, the optimal or suboptimal specific management measure solutions are identified, and specific management measure recommendations are generated.

5. The method according to claim 1, characterized in that According to the ecological change trend forecast report, the maintenance operation time and maintenance work intensity as well as the tourist guidance strategy in the garden are automatically adjusted, and the particle swarm optimization algorithm is used to find the optimal solution for automatic adjustment. Based on the garden ecological health and tourist experience analysis in the garden ecological portrait, a resource allocation plan and a tour experience optimization plan are generated, including: Utilizing the ecological change trend forecast report, the current and short-term future ecological conditions and potential changes are analyzed and processed, the types of maintenance operations and tourist guidance strategies that need to be adjusted are identified, and a list of adjustment requirements is obtained; According to the adjustment demand list, combined with the historical maintenance operation time and maintenance work intensity, the particle swarm optimization algorithm is applied to automatically adjust the maintenance operation time and maintenance work intensity and the tourist guidance strategy under the premise of meeting the resource constraint conditions, find the optimal solution for automatic adjustment, and generate a preliminary adjustment plan; Based on the preliminary adjustment plan, the garden ecological health data in the garden ecological portrait is used to evaluate the effect of the adjusted maintenance operation to obtain an ecological protection evaluation result; Based on the analysis of the visitor experience in the garden ecological portrait, the influence of the adjusted visitor guidance strategy on visitor satisfaction is evaluated to obtain the visitor experience evaluation result; Integrate the ecological protection assessment results and the tourist experience assessment results, further optimize the preliminary adjustment plan, and generate a resource allocation plan and a tour experience optimization plan.

6. The method according to claim 5, characterized in that The analysis of the visitor experience in the garden ecological portrait is based on evaluating the impact of the adjusted visitor guidance strategy on visitor satisfaction, and obtaining visitor experience evaluation results, including: Utilizing the tourist experience data in the garden ecological portrait, extracting multiple indicators related to tourist satisfaction, and forming a tourist experience indicator system; According to the tourist experience index system and the adjusted tourist guidance strategy, the tourist flow of each scenic spot and area in the garden is predicted and processed, the tourist flow in different time periods is simulated, and the tourist flow simulation results are generated; Based on the tourist flow simulation results, a user experience scoring model is applied to comprehensively consider factors such as tourist stay time, queue waiting time, and crowding level, calculate the tourist satisfaction score of each attraction or area, and obtain a preliminary tourist satisfaction assessment; The preliminary visitor satisfaction evaluation is used in combination with historical visitor feedback data and social media evaluation information to conduct multi-source data analysis, calibrate and optimize the visitor satisfaction score, and generate a visitor experience evaluation result.

7. The method according to claim 1, characterized in that Based on the resource allocation plan and the tour experience optimization plan, a continuous learning mechanism is established, a regular review process is implemented, and a management system configuration is generated, including: Execute the resource allocation plan and tour experience optimization plan, track and monitor the actual implementation effect, collect multi-source data on maintenance operation execution, resource utilization efficiency and tourist feedback, and obtain implementation effect monitoring data; Based on the execution effect monitoring data and combined with the dynamic change information in the garden ecological portrait, an intelligent decision-making algorithm is applied to analyze the deviation between the actual execution effect and the expected execution effect, identify areas where the execution effect needs to be improved, and generate a deviation analysis report; Based on the deviation analysis report, design and implement a regular review process to review the effectiveness of resource allocation plans and tour experience optimization plans, and obtain regular review results; The periodic review results are used to update parameter settings in the intelligent decision-making algorithm, adjust resource allocation and tourist guidance strategies, introduce impact assessments of socio-economic factors, and generate management system configurations.

8. A smart garden management system based on big data technology, characterized in that: include: The acquisition module is used to collect multi-source heterogeneous data in the garden in real time through the environmental perception network, and integrate and compare the multi-source heterogeneous data in combination with the geographic information system and time series analysis to obtain the garden ecological portrait; An evaluation module is used to evaluate the current ecological health status of the garden by using the garden ecological portrait, and to predict the ecological change trend that may occur in the short term by applying the Bayesian network algorithm, and to generate an ecological change trend prediction report by introducing a multi-criteria decision analysis method combined with scenario simulation technology; An adjustment module is used to automatically adjust the maintenance operation time and maintenance work intensity and the tourist guidance strategy in the garden according to the ecological change trend forecast report, use a particle swarm optimization algorithm to find the optimal solution for automatic adjustment, and generate a resource allocation plan and a tour experience optimization plan based on the garden ecological health and tourist experience analysis in the garden ecological portrait; A module is established to establish a continuous learning mechanism, implement a regular review process, and generate a management system configuration based on the resource allocation plan and tour experience optimization plan.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a smart garden management method based on big data technology as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a smart garden management method based on big data technology as described in any one of claims 1 to 7 is implemented.

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