An ecological assessment method and system for garden plant environmental monitoring

Abstract

The present application provides an ecological assessment method and system for monitoring the environment of garden plants. Among them, multiple environmental parameters related to the health status of plants in the garden environment collected in real time by the sensor network are received; the multiple environmental parameters are analyzed using a pre-set ecological health indicator system, and the ecological environment quality of garden plants is dynamically evaluated to form a preliminary ecological health portrait; based on the preliminary ecological health portrait, the ecological pressure trend of garden plants in the future cycle is predicted in combination with historical ecological data and seasonal changes; multiple ecological intervention measures are formulated and simulated, and the ecological environment improvement effect under each ecological intervention measure is evaluated; the optimal ecological intervention measure is determined based on the evaluation results, and the optimal ecological intervention measure is fed back to the garden management department to guide ecological maintenance actions. The technical solution provided by this application can effectively improve the management level of garden plants.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of garden management technology, and in particular to an ecological assessment method and system for garden plant environment monitoring. Background Art

[0002] With the acceleration of urbanization, the management and maintenance of urban green spaces and garden plants have become particularly important. Garden plants are not only an important part of the urban ecosystem, but also play an irreplaceable role in improving the quality of the urban environment and the quality of life of residents. However, the traditional management of garden plants mainly relies on manual inspections and empirical judgments, which makes it difficult to achieve comprehensive and real-time monitoring of the plant growth environment. Therefore, there is an urgent need for a technical solution that can monitor the growth environment of garden plants in real time, dynamically evaluate plant health, and predict future ecological pressures to guide scientific ecological maintenance actions.

[0003] At present, environmental monitoring of garden plants mainly relies on the following methods:

[0004] Manual inspection: Garden managers regularly observe and record plants to understand their growth status and environmental conditions. This method is time-consuming and laborious, and is greatly affected by personal experience and subjective judgment.

[0005] Fixed monitoring stations: Environmental monitoring stations are set up at specific locations to collect environmental parameters such as temperature, humidity, and light. Although it can provide relatively accurate data, the monitoring range is limited and it is difficult to cover the entire garden area.

[0006] Initial application of IoT technology: Some gardens have begun to try to use sensor networks to collect environmental data, but these systems usually lack comprehensive ecological health assessment and prediction functions and are mainly used for simple data recording and alarm.

[0007] The defects of the existing solutions are:

[0008] Limited monitoring scope and frequency: Traditional manual inspections and fixed monitoring station methods cannot achieve comprehensive and real-time monitoring of the entire garden area, and are prone to missing local environmental changes, resulting in untimely detection of problems.

[0009] Lack of comprehensive assessment and prediction capabilities: Although existing sensor network applications can collect a large amount of environmental data, they lack an effective ecological health indicator system and prediction model, and are unable to dynamically evaluate the ecological environmental quality of plants, let alone predict future ecological pressure trends.

[0010] Insufficient decision support: Existing monitoring methods and systems mainly provide data recording and simple alarm functions, lack of scientific ecological intervention measures formulation and evaluation mechanism, and it is difficult to effectively guide the maintenance actions of garden management departments. Summary of the invention

[0011] The embodiments of the present application provide an ecological assessment method and system for garden plant environment monitoring, which are used to solve the problem of low garden management level in the prior art.

[0012] In a first aspect, the present application provides an ecological assessment method for garden plant environment monitoring, comprising:

[0013] Receive multiple environmental parameters related to plant health status in the garden environment collected in real time by the sensor network;

[0014] Analyze the multiple environmental parameters using a pre-set ecological health indicator system, dynamically evaluate the ecological environment quality of garden plants, and form a preliminary ecological health portrait;

[0015] Based on the preliminary ecological health portrait, historical ecological data and seasonal variation patterns are combined to predict the ecological stress trend of garden plants in future cycles;

[0016] According to the ecological pressure trend, formulate and simulate multiple ecological intervention measures, and evaluate the ecological environment improvement effect under each ecological intervention measure;

[0017] The optimal ecological intervention measures are determined based on the evaluation results, and are fed back to the garden management department to guide ecological maintenance actions.

[0018] In a second aspect, the present application embodiment provides an ecological assessment system for garden plant environment monitoring, comprising:

[0019] A receiving module, used for receiving multiple environmental parameters related to plant health status in a garden environment collected in real time by a sensor network;

[0020] An evaluation module, for analyzing the multiple environmental parameters using a preset ecological health indicator system, dynamically evaluating the ecological environment quality of garden plants, and forming a preliminary ecological health portrait;

[0021] A prediction module is used to predict the ecological pressure trend of garden plants in the future cycle based on the preliminary ecological health portrait, combined with historical ecological data and seasonal variation patterns;

[0022] Formulate a simulation module for formulating and simulating a plurality of ecological intervention measures according to the ecological pressure trend, and evaluate the ecological environment improvement effect under each ecological intervention measure;

[0023] The feedback module is used to determine the optimal ecological intervention measures based on the evaluation results, and to feed back the optimal ecological intervention measures to the garden management department to guide ecological maintenance actions.

[0024] 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 an ecological assessment method for garden plant environment monitoring as described in any one of the first aspects above.

[0025] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements an ecological assessment method for garden plant environment monitoring as described in any one of the first aspects above.

[0026] In the embodiment of the present application, multiple environmental parameters related to the health status of plants in the garden environment collected in real time by the sensor network are received; the multiple environmental parameters are analyzed using a pre-set ecological health indicator system, the ecological environment quality of garden plants is dynamically evaluated, and a preliminary ecological health portrait is formed; based on the preliminary ecological health portrait, the ecological pressure trend of garden plants in the future cycle is predicted in combination with historical ecological data and seasonal variation laws; multiple ecological intervention measures are formulated and simulated, and the ecological environment improvement effect under each ecological intervention measure is evaluated; the optimal ecological intervention measure is determined based on the evaluation results, and the optimal ecological intervention measure is fed back to the garden management department to guide ecological maintenance actions. The technical solution provided by this application can effectively improve the management level of garden plants and promote ecological environmental protection and social sustainable development on a larger scale.

[0027] 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

[0028] 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.

[0029] Figure 1 A flow chart of an ecological assessment method for garden plant environmental monitoring provided in an embodiment of the present application;

[0030] Figure 2 A schematic diagram of the structure of an ecological assessment system for garden plant environment monitoring provided in an embodiment of the present application;

[0031] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 1 A flowchart of an ecological assessment method for garden plant environmental monitoring is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0036] 101. Receive multiple environmental parameters related to plant health status in the garden environment collected in real time by the sensor network;

[0037] This step involves collecting multiple environmental parameters closely related to plant health status in real time through a sensor network deployed in the garden. These data are the basis for subsequent analysis and evaluation, and are crucial for dynamically monitoring the plant growth environment, timely detecting potential problems, and formulating scientific management measures.

[0038] Sensor network: A network system consisting of multiple sensor nodes that can work together to continuously collect and transmit environmental data.

[0039] Real-time collection: refers to the sensor network continuously collecting data and immediately transmitting the data to the central processing system or cloud server to achieve instant data analysis and processing.

[0040] Multi-environmental parameters: include a range of environmental factors that affect plant health, such as temperature, humidity, light intensity, soil moisture, soil pH, carbon dioxide concentration in the air, etc.

[0041] Plant health status: refers to the overall health level of a plant during its growth process, including growth rate, leaf color, pest and disease conditions, etc.

[0042] Multiple sensor nodes are deployed in the garden to ensure coverage of key locations throughout the area, such as around different types of plants, near irrigation systems, and important landscape nodes.

[0043] Sensor types include but are not limited to: temperature sensors, humidity sensors, light intensity sensors, soil moisture sensors, soil pH sensors, carbon dioxide concentration sensors, and wind speed and direction sensors.

[0044] Set the frequency of data collection according to specific needs. Usually, data can be collected every 10 minutes to every hour to ensure the timeliness and accuracy of the data.

[0045] Data transmission: Sensor nodes transmit the collected data to the central processing system or cloud server through wireless communication technologies (such as Zigbee, LoRa, Wi-Fi). The security and integrity of the data must be guaranteed during the data transmission process to prevent data loss or tampering.

[0046] Data storage: The collected data is stored in a central processing system or cloud server. Data storage should be structured to facilitate subsequent data processing and analysis. The stored data should be accompanied by timestamps and location information to facilitate tracking and analysis of environmental changes at a specific time and place.

[0047] Data preprocessing: Perform preliminary preprocessing on the collected data, including noise removal, missing value filling, data smoothing, etc., to improve data quality. Data preprocessing helps the subsequent analysis and modeling process to ensure the reliability and accuracy of the data.

[0048] Suppose a park management department wants to monitor the plant growth environment in the park in real time. The following are the specific implementation steps:

[0049] Sensor nodes were installed at key locations in the park, each containing sensors for temperature, humidity, light intensity, soil moisture, and soil pH.

[0050] The sensor nodes are connected to the central processing system via LoRa wireless communication technology.

[0051] Set data collection every 15 minutes to ensure the real-time and accuracy of the data.

[0052] The sensor nodes transmit data to the central processing system through the LoRa network.

[0053] After the central processing system receives the data, it immediately stores and performs preliminary processing.

[0054] The collected data is stored in a central database, and each record contains a timestamp, location information, and values ​​of various environmental parameters.

[0055] The database adopts a structured design to facilitate subsequent data query and analysis.

[0056] The collected data are preprocessed to remove outliers and noise and fill in missing values.

[0057] The results of data preprocessing are used for subsequent ecological health assessment and prediction.

[0058] By collecting multiple environmental parameters related to plant health in the garden environment in real time, the following beneficial effects can be achieved: Real-time data collection reduces the workload of manual inspections and improves monitoring efficiency. High-frequency data collection can accurately reflect changes in environmental parameters and provide a reliable data basis for subsequent analysis. Through real-time data monitoring, environmental parameter anomalies can be quickly discovered, and countermeasures can be taken in time to ensure plant health. Based on the analysis results of real-time data, garden management departments can make more scientific and accurate management decisions, optimize resource allocation, and improve management levels.

[0059] In summary, this step is the basis of the entire ecological assessment method. By collecting multiple environmental parameters in real time, it provides solid data support for subsequent dynamic assessment, prediction and intervention.

[0060] 102. Analyze the multiple environmental parameters using a pre-set ecological health indicator system, dynamically evaluate the ecological environment quality of garden plants, and form a preliminary ecological health portrait;

[0061] Ecological health index system: refers to a scientifically verified standard system used to evaluate the quality of the environment in which garden plants live. This system usually includes a series of key indicators, such as temperature, humidity, light intensity, soil pH, soil moisture, air quality, etc.

[0062] Dynamic assessment: refers to the continuous updating of ecological environment quality assessment results based on real-time collected environmental parameters, combined with historical data and current seasonal characteristics.

[0063] Preliminary ecological health portrait: refers to an overview of the current ecological environment quality of garden plants, formed by comprehensive analysis of multiple environmental parameters and ecological health indicators, including health status, potential risk points, etc.

[0064] In a specific embodiment, we continue to take the large public garden of a city park administration as an example. The garden has deployed a sensor network and is collecting environmental parameters in real time. Next, we will show how to use the preset ecological health indicator system to analyze these data and dynamically evaluate the ecological environment quality of garden plants, and finally form a preliminary ecological health portrait.

[0065] Set a series of ecological health indicators, including:

[0066] Temperature: Suitable range is 15℃-30℃;

[0067] Humidity: The suitable range is 40%-80%;

[0068] Light intensity: Set the appropriate light intensity range according to different plant types;

[0069] Soil pH: The optimum range is 6.0-7.5;

[0070] Soil moisture: The suitable range is 20%-40%;

[0071] Air Quality Index (AQI): The optimum range is 0-50 (good).

[0072] The multivariate environmental parameters collected in real time are mapped to the above ecological health indicators and standardized to meet the unified evaluation standards. For example, if the soil moisture is 35%, it is considered to be within the appropriate range and the score is 1; if it is 15%, it is considered too low and the score is 0.

[0073] According to the importance of each indicator and their mutual relationship, a comprehensive score reflecting the quality of the ecological environment is calculated. For example, assuming that the importance weight of temperature is 0.2, humidity is 0.3, light intensity is 0.1, soil pH is 0.2, soil moisture is 0.1, and air quality index is 0.1.

[0074] Based on comprehensive rating , the ecological environment quality is divided into several levels, such as:

[0075] 0.8 and above: excellent;

[0076] 0.6-0.8: good;

[0077] 0.4-0.6: normal;

[0078] Below 0.4: Poor.

[0079] Through this grading method, we can intuitively understand the quality of the current ecological environment.

[0080] Based on the above assessment results, a preliminary ecological health portrait is formed to show the current ecological environment quality level, the status of key indicators and potential risk points.

[0081] For example, assuming the comprehensive score is 0.7, the preliminary ecological health portrait shows that the ecological environment quality is "good", specifically: the temperature, humidity, and soil pH are all within the appropriate range, but the soil humidity is slightly low, and the air quality index is good.

[0082] This application takes into account that although a variety of environmental parameters can be collected, there is often a lack of in-depth analysis and comprehensive evaluation of these data. In order to solve the above problems, the embodiment of this application proposes an optional solution, which maps and standardizes the multivariate environmental parameters through a preset ecological health indicator system, and calculates a comprehensive score based on the importance of each indicator and its interactive relationship, thereby constructing a preliminary ecological health portrait. In this way, the ecological environment quality of garden plants can be evaluated more comprehensively and accurately, and intuitive visualization results can be provided to help garden management departments make scientific decisions.

[0083] Optionally, the step 102 of "using a preset ecological health indicator system to analyze the multivariate environmental parameters, dynamically evaluate the ecological environment quality of garden plants, and form a preliminary ecological health portrait" includes: based on a preset ecological health indicator system, mapping the collected multivariate environmental parameters to corresponding ecological health indicators, the ecological health indicator system covers the key environmental factors required for plant growth; standardizing the mapped ecological health indicators; calculating a comprehensive score reflecting the ecological environment quality of garden plants based on the importance of the ecological health indicators and the interaction between the ecological health indicators; and dividing different ecological environment quality levels based on the calculated comprehensive score to construct a preliminary ecological health portrait of garden plants, which intuitively displays the health status and potential risk points of the environment in which the plants are located.

[0084] Among them, the ecological health indicator system: a set of scientifically verified standard systems used to evaluate the quality of the environment in which garden plants are located. Mapping: converting the collected environmental parameters into values ​​corresponding to ecological health indicators. Standardization processing: converting data of different dimensions into a unified standard scale for subsequent analysis. Importance weight: a quantitative representation of the degree of impact of each ecological health indicator on the overall ecological environment quality. Interaction relationship: the interaction relationship between different ecological health indicators. Comprehensive score: a comprehensive evaluation value calculated based on each ecological health indicator and its weight. Ecological environment quality level: different ecological environment quality levels divided according to the comprehensive score. Preliminary ecological health portrait: a chart or report that intuitively shows the health status of the plant's environment and potential risk points.

[0085] In the embodiment of the present application, for example, the multivariate environmental parameters (such as temperature, humidity, light intensity, etc.) collected in real time are mapped to preset ecological health indicators. For example, temperature data is mapped to the indicator of "suitable temperature range". The mapped ecological health indicators are standardized to ensure that all indicators are on the same scale. For example, data such as temperature and humidity are converted to standardized values ​​between 0 and 1. Based on the calculated comprehensive score , and divide the ecological environment into different levels of quality. For example, 0.8 and above is excellent, 0.6-0.8 is good, 0.4-0.6 is fair, and below 0.4 is poor. Based on the above assessment results, a preliminary ecological health portrait is formed to show the current ecological environment quality level, the status of each key indicator and potential risk points.

[0086] Suppose a sensor network is deployed in a green area in a park to collect environmental parameters in real time. The following are the specific implementation steps:

[0087] The data collected by the sensor are as follows:

[0088] Temperature: 28℃

[0089] Humidity: 70%

[0090] Light intensity: 1500 lux

[0091] Soil pH: 6.5

[0092] Soil moisture: 30%

[0093] Air Quality Index (AQI): 45

[0094] The standardized data is as follows:

[0095] Temperature: 0.6 (assuming the suitable range is 25-30°C)

[0096] Humidity: 0.75

[0097] Light intensity: 0.75 (assuming the appropriate range is 1000-2000 lux)

[0098] Soil pH: 0.75

[0099] Soil moisture: 0.75

[0100] Air quality index: 0.9 (assuming the appropriate range is 0-50)

[0101] Assume that the importance weights of each indicator are:

[0102] Temperature: 0.2

[0103] Humidity: 0.3

[0104] Light intensity: 0.1

[0105] Soil pH: 0.2

[0106] Soil moisture: 0.1

[0107] Air Quality Index: 0.1

[0108] Correlation Coefficient Matrix as follows:

[0109] ;

[0110] Substitute the formula to calculate the comprehensive score :

[0111] ;

[0112] The overall score is 1.6635, which is an “excellent” grade.

[0113] The preliminary ecological health portrait shows that the ecological environment quality of the green area is "excellent", all indicators are within the appropriate range, and there are no obvious potential risk points.

[0114] The above method can not only comprehensively and accurately evaluate the ecological environment quality of garden plants, but also provide an intuitive ecological health portrait, helping garden management departments to promptly discover and solve potential problems. In addition, this method can also improve management efficiency, reduce the workload of manual inspections, and realize intelligent and refined garden management.

[0115] 103. Based on the preliminary ecological health portrait, combined with historical ecological data and seasonal variation patterns, predict the ecological stress trend of garden plants in the future cycle;

[0116] This step aims to use the constructed preliminary ecological health portrait, historical ecological data and seasonal variation patterns to predict the ecological pressure that garden plants may face in the future. Through such predictions, we can prepare in advance and take appropriate management measures to mitigate adverse effects.

[0117] Preliminary ecological health portrait: a comprehensive assessment result reflecting the current ecological environment quality formed through the previous step, including the status of environmental parameters, comprehensive scores and potential risk points.

[0118] Historical ecological data: refers to environmental parameter data collected over a period of time in the past. These data record changes in the plant growth environment, including temperature, humidity, light intensity, etc.

[0119] Seasonal change patterns: refers to the influence of different seasons of the year on the plant growth environment. For example, the budding period in spring requires suitable temperatures, high temperature and drought in summer may affect water supply, soil moisture needs to be paid attention to during the leaf fall period in autumn, and low temperature and frost damage in winter may affect plant survival.

[0120] Ecological pressure trend: refers to the changing trend of the ecological environmental pressure faced by plants in the future, such as pest and disease risks, drought, floods, etc.

[0121] Extract the current status information of plants and their environment from the preliminary ecological health portrait, including but not limited to key indicators such as plant health status, environmental suitability, and pest and disease incidence.

[0122] Collect and organize data on environmental parameter records (such as temperature, humidity, light), plant growth conditions, and the occurrence of pests and diseases over the past few growth cycles.

[0123] Analyze this data to identify long-term trends and cyclical patterns of change.

[0124] According to the laws of seasonal changes, identify the key ecological factors of each season, such as budding temperature in spring, high temperature and drought in summer, leaf fall and humidity in autumn, and low temperature and frost damage in winter.

[0125] Establish a database containing these seasonal ecological factors for subsequent use.

[0126] Using time series analysis methods, historical ecological data are modeled to capture the temporal patterns and trends of plant ecological stress.

[0127] This can be achieved with statistical models such as ARIMA or machine learning models such as LSTM.

[0128] Combine information on current ecological status, a database of seasonal ecological factors, and a time series model of historical ecological data.

[0129] Use machine learning algorithms (such as random forests, support vector machines, etc.) or other predictive models to predict the trends in ecological stress that plants may face in future cycles.

[0130] In this embodiment of the application, it is assumed that a group of cherry trees in a park has experienced varying degrees of drought and pest problems in the past few years. In order to better predict future ecological pressures, the park management department has taken the following steps:

[0131] According to the latest sensor data and ecological health portraits, we know that the current soil moisture is low and some trees have minor signs of pests and diseases. We reviewed the growth data of cherry trees in the past five years and found that there is a certain degree of drought every summer, and a more serious outbreak of pests and diseases occurs every two years or so. We determined that the minimum temperature required for spring budding is 10°C, the soil moisture needs to be kept above 30% during high temperature and drought in summer, appropriate humidity is required during the fall leaf period to prevent fungal infection, and low temperature frost damage needs to be prevented in winter. The ARIMA model was used to model the historical temperature and humidity data, and it was found that there is an obvious drought year every three years. Combining the current ecological status, seasonal ecological factors and the results of the ARIMA model, the random forest algorithm is used to predict that severe drought may occur again in the next two years, and the risk of pests and diseases outbreaks is expected in the next year. By predicting future ecological pressures, response strategies can be formulated in advance to reduce losses caused by emergencies. Reasonable arrangements for management work such as irrigation, fertilization, pest and disease control can improve resource utilization efficiency. Decisions made based on data analysis and prediction results are more scientific and reliable, which helps to improve the overall level of garden management.

[0132] By taking this step, garden managers can better anticipate future challenges and prepare in advance, ensuring that plants can grow in optimal condition.

[0133] This application takes into account that although it is possible to collect and analyze current environmental parameters in real time, it lacks the ability to predict future ecological pressure trends. In order to solve the above problems, the embodiment of the present invention proposes an optional solution, which combines the preliminary ecological health portrait, long-term historical ecological data and seasonal variation patterns, and uses time series analysis and machine learning algorithms to predict ecological pressure trends in future cycles. This can improve the accuracy of the prediction, help garden management departments better plan future management strategies, and reduce the adverse effects caused by environmental changes.

[0134] Optionally, the step 103 of "predicting the trend of ecological stress of garden plants in future cycles based on the preliminary ecological health portrait and combining historical ecological data with seasonal variation patterns" includes: obtaining and organizing the current ecological environment quality information reflected in the preliminary ecological health portrait, including key indicators of plant health status, environmental suitability, and incidence of pests and diseases; collecting and analyzing long-term historical ecological data related to the garden plants, the historical ecological data at least covering environmental parameter records, plant growth conditions, and occurrence of pests and diseases in the past several growth cycles; identifying key ecological factors affecting the growth of garden plants in combination with seasonal variation patterns, the key ecological factors including bud temperature in spring, high temperature and drought in summer, leaf fall humidity in autumn, and low temperature and frost damage in winter, so as to establish a seasonal ecological factor database; using time series analysis methods to model the historical ecological data to capture the temporal patterns and trends of ecological stress of garden plants; combining the current ecological status information in the preliminary ecological health portrait, the seasonal ecological factor database, and the time series model of the historical ecological data, and using a machine learning algorithm to predict the trend of ecological stress faced by garden plants in future cycles.

[0135] Seasonal ecological factors: refers to the key environmental factors that have a significant impact on plant growth in different seasons, such as spring germination temperature, summer high temperature and drought, autumn leaf fall and humidity, and winter low temperature and frost damage.

[0136] Time series analysis: A statistical method used to analyze data that changes over time and capture its patterns and trends.

[0137] Machine learning algorithms: A type of algorithm that automatically learns and improves predictive models through training data, such as random forests and support vector machines.

[0138] Key indicators regarding plant health status (e.g. leaf color, branch development), environmental suitability (e.g. light intensity, air quality), and the incidence of pests and diseases are extracted from the preliminary ecological health portrait.

[0139] Collect historical ecological data covering at least several complete growth cycles, including but not limited to daily / monthly average temperature, rainfall, wind speed and other environmental parameters; also record the specific growth performance of plants and any known pest and disease events during the same period.

[0140] Analyze the characteristics of the impact of different seasons on plant growth, determine important factors such as the minimum temperature required during the germination period and the ideal soil moisture level during the summer drought, and build a special seasonal ecological factor library based on this.

[0141] Apply appropriate time series analysis tools (e.g. ARIMA model) to train the model based on the historical data in the second step in order to capture the pattern of ecological pressure faced by plants over time.

[0142] The latest ecological conditions obtained in the first step, the knowledge base of seasonal factors formed in the third step, and the time series model obtained in the fourth step are combined to use appropriate machine learning techniques (such as random forests) to predict possible stress conditions in the next few growth cycles.

[0143] In the embodiment of the present application, it is assumed that a city park wants to improve the maintenance of its internal cherry tree group. First, managers will know from the results of the most recent ecological health check that the trees are generally in good condition, but there are slight signs of aphid infestation. Then, they reviewed the relevant data over the past decade and noticed that there would be a more serious aphid outbreak every four years or so. In addition, problems such as delayed flowering due to low temperatures in spring and leaf fall caused by extreme high temperatures in summer were also found. Based on these observations, the team defined a series of key ecological factors and used the ARIMA model to analyze the changing trends of temperature and precipitation. Finally, the support vector machine (SVM) algorithm was used to integrate all available information, and the main ecological challenges that might occur in the next three years were successfully predicted-large-scale aphid infestations are expected to occur again in two years, and at the same time, it is reminded to guard against possible heat waves in the following summer. This prediction enabled the park to take measures in advance to strengthen prevention, such as adjusting irrigation plans and preparing sufficient pesticide reserves, thereby effectively reducing the actual degree of damage.

[0144] By implementing the above solutions, garden managers can not only more accurately predict future problems, but also optimize resource allocation and improve work efficiency based on the predicted results. This not only helps to maintain the optimal growth state of plants, but also reduces the additional expenses caused by unexpected situations, reflecting the significant advantages of data-driven decision-making.

[0145] 104. Based on the ecological pressure trend, formulate and simulate multiple ecological intervention measures, and evaluate the ecological environment improvement effect under each ecological intervention measure;

[0146] Based on the predicted results of the ecological pressures (such as temperature, humidity, pests and diseases, etc.) that garden plants may face in the future cycle, combined with the physiological characteristics and ecological needs of plants, a variety of possible intervention measures are designed. These measures may include but are not limited to irrigation strategy adjustment, fertilization plan optimization, shading facility installation, pest and disease control methods, etc.

[0147] For each ecological intervention designed, models or simulation software are used to simulate what would happen to the plants and their surroundings if the intervention were implemented. Such simulations can help understand how a particular intervention affects key ecological parameters and predict changes in plant health and growth.

[0148] Based on the simulation results, the ecological environment improvement effect of each intervention measure is evaluated. This usually involves comparing the expected improvement in plant growth conditions under different measures, such as changes in water use efficiency, soil nutrient levels, and pest and disease incidence.

[0149] Other factors such as economic cost, difficulty of implementation, and sustainability may also be considered in the assessment to ensure that the measures selected are not only effective but also feasible.

[0150] A multi-objective optimization algorithm is used to comprehensively evaluate all simulation results to find one or several intervention measures with the best comprehensive benefits (i.e., the most effective way to alleviate ecological pressure while meeting other constraints) as the optimal solution recommended to the garden management department.

[0151] Through such a process, a scientific basis can be provided for garden management, helping decision makers make more reasonable ecological maintenance decisions, thereby promoting the healthy growth of garden plants and improving the ecological environment quality of the entire garden area.

[0152] This application takes into account that experience-based management and intervention measures are usually adopted. This approach often lacks an accurate grasp of the dynamic changes in the ecological environment and an effective prediction of future ecological pressures. Therefore, when faced with complex situations such as climate change and outbreaks of pests and diseases, traditional methods may not be able to respond in a timely and effective manner, resulting in waste of resources or poor ecological restoration effects. In order to solve the above problems, the embodiments of the present invention propose a further optional solution, which aims to formulate more effective and economical ecological intervention measures through scientific analysis and simulation to improve the quality of the growth environment of garden plants.

[0153] Optionally, the step 104 of "formulating and simulating multiple ecological intervention measures based on the ecological pressure trend, and evaluating the ecological environment improvement effect under each of the ecological intervention measures" includes: designing multiple ecological intervention measures based on the predicted ecological pressure trend and combining the physiological characteristics and ecological needs of garden plants; for each ecological intervention measure, simulating the response changes of the garden plants and their surrounding environment after the implementation of the measure; and evaluating the ecological environment improvement effect under each of the ecological intervention measures based on the simulation results.

[0154] Ecological pressure trend: refers to the changing trend of unfavorable conditions for plant survival caused by natural factors (such as climate) or human activities.

[0155] Physiological characteristics: refers to the intrinsic biological properties of plants in response to changes in the external environment.

[0156] Ecological requirements: suitable environmental conditions required by plants to maintain normal growth and development.

[0157] Ecological intervention measures: refers to artificial means taken to improve the plant growth environment, such as irrigation, fertilization, etc.

[0158] Response change: The change in the state of plants and their surroundings after the implementation of ecological intervention measures.

[0159] Ecological environment improvement effect: refers to the actual impact of the implementation of ecological intervention measures on improving plant health and surrounding environmental quality.

[0160] Design multiple ecological intervention measures: Based on the predicted results of future ecological pressures, combined with the physiological characteristics and ecological needs of specific plants, a series of possible intervention measures are proposed. For example, if the forecast shows that there will be severe drought in the summer, then you can consider increasing the frequency of irrigation or building a drip irrigation system; if it is predicted that the humidity in the fall is high, which may lead to fungal diseases, you need to prepare a fungicide spraying plan in advance.

[0161] Simulate response changes: Use computer models to simulate the specific impact of each intervention. This step may involve using hydrological models to predict water distribution and nutrient transport models to estimate changes in soil nutrient levels.

[0162] Evaluate the improvement effect: Analyze the data obtained from the simulation and evaluate the effect of each intervention measure. This includes but is not limited to improvements in plant survival rate, growth rate, probability of pests and diseases, etc.

[0163] In the embodiment of this application, it is assumed that a group of precious tree species are planted in a park. After data analysis, it is found that the area may experience continuous high temperature and drought in the next few years, which poses a potential threat to the trees. Based on this information:

[0164] Design Interventions: Two main intervention strategies were proposed - installing a smart drip irrigation system and adding shade nets.

[0165] Simulating response changes: The simulation results of the smart drip irrigation system show that it can effectively maintain soil moisture within an appropriate range and reduce water waste. The sunshade net can significantly reduce the surface temperature and protect the trees from the strong direct sunlight.

[0166] Evaluating the improvement effect: By comparing the comprehensive benefits of the two measures, it was found that although the smart drip irrigation system requires a larger initial investment, it can save more water resources in the long run and is more beneficial to tree growth.

[0167] Through such a process, not only the scientificity and accuracy of ecological management decisions are improved, but also the selected measures are ensured to be both efficient and sustainable. The direct benefit of this is to reduce unnecessary expenses, while ensuring the good growth of garden plants to the greatest extent, and solving the problems caused by the lack of accurate prediction and evaluation in traditional methods.

[0168] 105. Determine the optimal ecological intervention measures based on the evaluation results, and provide feedback to the garden management department to guide ecological maintenance actions.

[0169] This step aims to select the most effective interventions based on the evaluation of the effects of different ecological interventions in the previous steps, and to provide feedback to the garden management department so that they can take appropriate actions to optimize the plant growth environment.

[0170] Evaluation results: refers to the evaluation of the effectiveness of different ecological intervention measures obtained through simulation and analysis.

[0171] The optimal ecological intervention measure: the best option selected among all the evaluated measures after comprehensively considering factors such as effectiveness, cost, and implementation difficulty.

[0172] Feedback: Provide the selected optimal measures to the department or person responsible for managing the garden in the form of a report or other form.

[0173] Ecological maintenance actions: Specific management and maintenance activities taken by the garden management department based on feedback, such as adjusting irrigation plans, fertilization strategies, etc.

[0174] Comprehensive evaluation: A comprehensive evaluation of each simulated ecological intervention measure is conducted, including its effect on ecological environment improvement, economic cost, feasibility of implementation and other factors.

[0175] Multi-objective optimization: Multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) are used to weigh various factors and determine one or more ecological intervention measures with the best comprehensive benefits as the optimal solution.

[0176] Develop detailed plans: Develop detailed implementation plans for the selected optimal measures, including specific operational steps, required resources, time schedule, and expected results.

[0177] Prepare a feedback report: Organize the selected best measures and their implementation plans into a written report, which should include background information, evaluation methods, simulation results, recommended measures and implementation details.

[0178] Communication and feedback: Communicate with the garden management department, introduce the report content, and answer related questions. Ensure that the management fully understands and agrees with the proposed measures.

[0179] Implementation and supervision: After the garden management department adopts the suggestions, assist or supervise the implementation of the measures to ensure that they are carried out according to the predetermined plan and make timely adjustments to cope with changes in actual operations.

[0180] In the present application example, it is assumed that a city park needs to take measures to restore the damaged lawn area after experiencing high temperature and drought in summer. After preliminary data collection, simulation and evaluation, the following are the specific implementation steps:

[0181] Three possible interventions were evaluated: increasing irrigation frequency, using water-retaining agents, and laying shade nets.

[0182] The simulation results show that the use of water retaining agents can effectively improve soil moisture retention capacity and reduce evaporation losses, while being moderately cost-effective and easy to implement.

[0183] Through a multi-objective optimization algorithm, factors such as cost, water conservation effect, and construction difficulty were comprehensively considered, and the use of water-retaining agent was finally selected as the optimal solution.

[0184] A detailed water retaining agent application plan has been formulated, including application amount, application method, application time, etc., and it is expected that soil moisture can be increased by 20% after application.

[0185] A detailed report was prepared, including background introduction, evaluation process, simulation results, recommended measures and specific implementation steps.

[0186] A meeting was held with the park management to present the report and answer questions regarding the selection of water retaining agent types and application techniques.

[0187] The park management department adopted the suggestion and started to purchase water retaining agent and arrange its application. The project team regularly checked the application effect and made fine adjustments according to the actual situation.

[0188] Through this series of steps, garden management departments can not only obtain support from scientific basis, but also continuously learn and improve in practice, so as to better maintain and manage the garden environment.

[0189] This application takes into account that the methods commonly used in current traditional solutions are based on single-objective optimization or rely on expert experience. In order to solve the above problems, the embodiment of the present invention proposes an optional solution that uses a multi-objective optimization algorithm to comprehensively evaluate the simulation results and feeds back the optimal ecological intervention measures to the garden management department in the form of electronic documents or reports. Through this method, more comprehensive, scientific and systematic ecological maintenance decisions can be achieved, thereby improving the efficiency and effectiveness of garden management.

[0190] Optionally, the "determining the optimal ecological intervention measures based on the evaluation results, and feeding back the optimal ecological intervention measures to the garden management department to guide ecological maintenance actions" in step 105 includes: using a multi-objective optimization algorithm to comprehensively evaluate the simulation results, and determining one or more ecological intervention measures with the best ecological environment improvement effect as the optimal solution; feeding back the optimal ecological intervention measures to the garden management department in the form of an electronic document or report to guide ecological maintenance actions.

[0191] Multi-objective optimization algorithm: A computational method that can simultaneously consider multiple objective functions (such as cost, effect, implementation difficulty, etc.) and find the optimal solution set. Common ones include multi-objective genetic algorithm, NSGA-II, etc.

[0192] Comprehensive evaluation: refers to the process of comprehensively evaluating different ecological intervention measures from multiple dimensions.

[0193] Electronic documents or reports: Organize the selected optimal ecological intervention measures into a format that is easy to read and understand for reference and implementation by the garden management department.

[0194] Multi-objective optimization algorithm: All simulated ecological intervention measures are evaluated using a multi-objective optimization algorithm (such as NSGA-II). The algorithm can find a balance between multiple conflicting objectives and generate a series of non-inferior solutions (Pareto frontier). Then one or several optimal solutions are selected based on the actual situation.

[0195] Optimal solution: From the set of non-inferior solutions obtained by the multi-objective optimization algorithm, combined with actual needs (such as budget constraints, resource availability, etc.), select one or several ecological intervention measures with the best ecological environment improvement effect as the final recommended solution.

[0196] Electronic document or report: The selected optimal ecological intervention measures and their detailed implementation plans shall be organized into electronic documents or reports. The report shall include background introduction, evaluation process, simulation results, recommended measures and specific implementation details.

[0197] Feedback: Submit the prepared electronic document or report to the garden management department, and conduct necessary communication and explanation to ensure that the management department fully understands and accepts the proposed measures.

[0198] In the present application example, it is assumed that a city park faces the problem of high temperature and drought in summer, and measures need to be taken to protect its precious trees. After preliminary data collection, simulation and evaluation, the following are the specific implementation steps:

[0199] Four possible intervention measures were evaluated using the NSGA-II algorithm: increasing the frequency of irrigation, using water retaining agents, laying shade nets, and adjusting the tree species layout. The simulation results showed that each measure had its own advantages and disadvantages in terms of cost, water conservation effect, and ease of construction. NSGA-II generated a Pareto frontier, showing a series of non-inferior solutions. Combined with the actual budget and resource situation of the park, two measures were selected as the optimal solutions: increasing the frequency of irrigation and using water retaining agents. These two measures achieved a good balance between cost and effect.

[0200] A detailed report was prepared covering:

[0201] Background: Impact of high temperature and drought in summer on trees.

[0202] Evaluation process: Application of multi-objective optimization algorithm and simulation results.

[0203] Recommended measures: Increase irrigation frequency and use water retaining agents.

[0204] Specific implementation details: specific arrangements for irrigation frequency, application rates and application methods of water retaining agents.

[0205] Feedback to the garden management department:

[0206] The report was sent to the park management department and a meeting was held to explain it in detail. In the meeting, questions such as the technical details of adjusting the irrigation frequency and the selection criteria of water retaining agents were answered.

[0207] The embodiment of the present application can find the optimal balance point between multiple conflicting objectives through a multi-objective optimization algorithm, avoiding the limitations of single-objective optimization. Provide detailed electronic documents or reports to ensure that the management department can clearly understand the recommended measures and their implementation details. By selecting the optimal intervention measures, limited resources can be used more effectively to maximize economic benefits. Through a multi-objective optimization algorithm, the optimal balance point can be found between multiple conflicting objectives, avoiding the limitations of single-objective optimization. Based on the analysis of data and models, the garden management department can make more scientific and reasonable decisions. Through continuous feedback and adjustment, the management strategy can be gradually optimized and the health of garden plants can be continuously improved.

[0208] Through this series of steps, the garden management department can not only obtain scientific support, but also continuously learn and improve in practice, so as to better maintain and manage the garden environment. This not only improves the growth quality of plants, but also improves the ecological environment of the entire garden area.

[0209] This application takes into account that in the existing garden plant ecological health assessment methods, a simple weighted average or single indicator evaluation method is usually used. In order to solve the above problems, this application proposes a method for calculating a comprehensive score based on the importance of ecological health indicators and their interrelationships. Through this method, the ecological environment quality of garden plants can be evaluated more comprehensively and scientifically, thereby providing more accurate management recommendations.

[0210] Optionally, the comprehensive score reflecting the ecological environment quality of garden plants is calculated based on the importance of the ecological health indicators and the interaction between the ecological health indicators, including: obtaining each ecological health indicator The value of , and each of the ecological health indicators With corresponding importance weights ;

[0211] The comprehensive score reflecting the ecological environment quality of garden plants is calculated using the following formula: :

[0212] ;

[0213] in, represents the number of ecological health indicators, Ecological health indicators The corresponding importance weights are, represents the actual measured value of the ecological health indicator, represents the summation expression, which represents the sum of the squares of all ecological health indicator weights and is used to standardize the comprehensive score. represents the standardization factor, which is used to prevent the comprehensive score from being distorted due to the excessive weight of ecological health indicators. represents the sum of weighted measurements of all ecological health indicators, represents the interaction between all ecological health indicators. Ecological health indicators and The correlation coefficient between The value range is ;

[0214] in, , represents the number of observations, and They are Ecological health indicators in the observation and The observed value of and Ecological Health Indicators and The average value is calculated as follows: .

[0215] Ecological health indicators ( ): refers to specific parameters used to measure the ecological environment quality of garden plants, such as temperature, humidity, light intensity, etc.

[0216] Importance Weight( ): Indicates the importance of each ecological health indicator on the overall ecological environment quality.

[0217] Correlation coefficient ( ): Measures the strength of the linear relationship between two ecological health indicators. The value range is [-1,1], where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.

[0218] Normalization factor ( ): An adjustment factor used to prevent the comprehensive score from being distorted due to excessive weighting of ecological health indicators.

[0219] The sum of weighted measurements ( ): The sum of the products of all ecological health indicators and their corresponding weights.

[0220] Interaction terms ( ): The impact of interactions between different ecological health indicators is taken into account.

[0221] In the present embodiment, it is assumed that a city park needs to evaluate the ecological environment quality of its cherry trees. The following are the specific implementation steps:

[0222] Identify three main indicators of ecological health: temperature ( ),humidity( ), soil pH value ( ).

[0223] Assign weight: Temperature = 0.5, humidity =0.3, soil pH =0.2.

[0224] Measurements: Temperature =25°C, humidity =70%, soil pH =6.5.

[0225] Assuming that multiple observations have been collected, calculate the correlation coefficient:

[0226] =0.8 (temperature and humidity are positively correlated)

[0227] =-0.5 (temperature is negatively correlated with soil pH)

[0228] =0.2 (humidity is weakly positively correlated with soil pH)

[0229] According to the formula:

[0230] ;

[0231] Calculation process:

[0232] Normalization Factor ;

[0233] Sum of weighted measurements ;

[0234] Interaction terms ;

[0235] ;

[0236] The comprehensive score is 56.335, indicating that the current ecological environment quality of the cherry tree group is in good condition.

[0237] Since temperature and humidity are positively correlated, and with higher temperatures, it may be necessary to focus on irrigation to maintain suitable humidity levels.

[0238] Since temperature is negatively correlated with soil pH, it is necessary to pay attention to changes in soil pH under high temperature conditions and adjust fertilization strategies in a timely manner.

[0239] Comprehensive assessment: Provides a more comprehensive assessment of ecological environment quality by considering the interrelationships between ecological health indicators.

[0240] Prevent weight distortion: The introduction of the normalization factor ensures that the overall score will not be distorted due to excessive weight of a certain indicator.

[0241] Improve efficiency: Identify potential problems in advance and take targeted measures to improve resource utilization efficiency.

[0242] Through this series of steps, garden management departments can not only obtain more accurate and comprehensive ecological environment quality assessments, but also formulate more effective management and maintenance measures based on this information, so as to better protect and promote the healthy growth of garden plants.

[0243] This application has taken into account the following problems in existing methods for predicting ecological stress of garden plants: many methods rely only on historical data for prediction, ignoring the current ecological status information and seasonal changes, resulting in inaccurate prediction results. Traditional time series models often cannot adapt well to dynamic changes in the environment, especially under the influence of climate change and sudden events (such as extreme weather). Simple statistical models may not be able to capture complex nonlinear relationships, thus affecting prediction accuracy.

[0244] In order to solve the above problems, this application proposes a method that combines a preliminary ecological health portrait, a seasonal ecological factor database, and a time series model of historical ecological data, and uses a machine learning algorithm to predict the trend of ecological pressure faced by garden plants in the future cycle. Through this method, future ecological pressure can be predicted more comprehensively and accurately, thereby providing more scientific management suggestions.

[0245] Optionally, the current ecological status information in the preliminary ecological health portrait, the seasonal ecological factor database and the time series model of the historical ecological data are combined to use a machine learning algorithm to predict the ecological pressure trend faced by garden plants in the future period, including: extracting the current ecological status information from the preliminary ecological health portrait , and obtain seasonal factors from the seasonal ecological factor database ;Use historical ecological data Build time series models based on trained machine learning models , combined with the current ecological status information ,Seasonal Factor , and time series of historical ecological data , predicting future ecological pressure ;

[0246] The predicted ecological pressure is calculated using the following formula: :

[0247] ;

[0248] in, is the adjustment coefficient, is the historical ecological data at each time point The contribution of is a constant, is the average value of historical ecological data, is the error term.

[0249] Preliminary Ecological Health Portrait( ): Refers to a comprehensive description formed after a comprehensive assessment of the current state of the plant and its environment, reflecting the overall health of the plant and the surrounding environmental conditions.

[0250] Seasonal ecological factors ( ): refers to factors that have an important impact on plant growth in different seasons, such as spring germination temperature, summer high temperature and drought, autumn leaf fall and humidity, and winter low temperature and frost damage.

[0251] Historical ecological data ( ): refers to all information records about the ecological environment of a specific area collected over a period of time in the past, including but not limited to meteorological conditions, soil characteristics, vegetation growth, etc.

[0252] Time series model: A statistical method used to analyze the relationship between data points arranged in time sequence to help discover underlying trends and patterns.

[0253] Machine Learning Models ): A type of computational model that can "learn" from data and make predictions or decisions based on that learning.

[0254] Adjustment coefficient ( ): Parameter used to adjust the impact of historical data on the forecast results.

[0255] Contribution( ): Indicates the contribution of each time point t in the historical ecological data to the prediction results.

[0256] constant( ): A fixed value used to adjust the impact of the average value of historical data.

[0257] Error term ( ): represents the deviation between the model prediction and the actual value.

[0258] In the present embodiment, it is assumed that a city park needs to predict the ecological pressure that its cherry trees may face in the next year. The following are the specific implementation steps:

[0259] Information such as the current health status of cherry trees, environmental suitability (such as light and humidity), and the incidence of pests and diseases was extracted from the preliminary ecological health portrait.

[0260] Assume current ecological status information =[0.8,0.6,0.2], representing good health status (0.8), moderate environmental suitability (0.6), and low incidence of pests and diseases (0.2), respectively.

[0261] Obtain key ecological factors of the current season from the seasonal ecological factor database.

[0262] Assuming it is spring, the seasonal factor =[15°C,70%], representing germination temperature (15°C) and relative humidity (70%), respectively.

[0263] Collect monthly ecological data for the past five years, including temperature, humidity, rainfall, etc.

[0264] The LSTM model is used to build a time series model to capture the changing trend of ecological pressure.

[0265] Trained machine learning models is a random forest model.

[0266] Historical ecological data

[0267] =[20,22,25,28,30,32,35,36,34,32,29,27,25,23,20,18,15,12,10,8,6,5,4,3,2,1,0.5], which represents the average monthly temperatures over the past five years.

[0268] Adjustment coefficient =0.5, constant =1.0.

[0269] Calculate the average of historical data =20.5.

[0270] According to the formula:

[0271] ;

[0272] Assume a machine learning model The output is 0.7, and the contribution of each time point They are [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.05, 0.02, 0.01, 0.005, 0.002, 0.001].

[0273] Calculation process:

[0274] ;

[0275] Assuming the error term , then the final predicted ecological pressure .

[0276] The forecast results show that the cherry tree cluster may face high ecological pressure in the coming year, especially under high temperature and drought conditions.

[0277] Park management departments can prepare in advance based on this and take appropriate irrigation, shading and pest control measures to reduce the impact of ecological pressure on cherry trees.

[0278] The embodiment of the present application provides a more comprehensive ecological pressure prediction by combining current ecological status information, seasonal factors and historical data. The machine learning model can better adapt to the dynamic changes of the environment and improve the prediction accuracy. Based on more accurate prediction results, management departments can make more scientific and reasonable decisions.

[0279] Through this series of steps, garden management departments can not only obtain more accurate and comprehensive predictions of ecological pressure, but also formulate more effective management and maintenance measures based on this information, so as to better protect and promote the healthy growth of garden plants.

[0280] Figure 2 The present application provides a schematic diagram of an ecological assessment system for monitoring the environment of garden plants. Figure 2 As shown, the device comprises:

[0281] A receiving module 21 is used to receive multiple environmental parameters related to plant health conditions in a garden environment collected in real time by a sensor network;

[0282] An evaluation module 22 is used to analyze the multi-environmental parameters using a preset ecological health indicator system, dynamically evaluate the ecological environment quality of garden plants, and form a preliminary ecological health portrait;

[0283] A prediction module 23 is used to predict the ecological pressure trend of garden plants in the future cycle based on the preliminary ecological health portrait, combined with historical ecological data and seasonal variation rules;

[0284] A simulation module 24 is used to formulate and simulate multiple ecological intervention measures according to the ecological pressure trend, and evaluate the ecological environment improvement effect under each ecological intervention measure;

[0285] The feedback module 25 is used to determine the optimal ecological intervention measures according to the evaluation results, and feed back the optimal ecological intervention measures to the garden management department to guide ecological maintenance actions.

[0286] Figure 2 The ecological assessment system for garden plant environment monitoring can be implemented Figure 1The implementation principle and technical effect of the ecological assessment method for garden plant environment monitoring described in the embodiment shown will not be repeated. The specific way in which each module and unit performs operations in the ecological assessment system for garden plant environment monitoring in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0287] In one possible design, Figure 2 The ecological assessment system for garden plant environment monitoring of 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;

[0288] 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 .

[0289] The processing component 32 is used to: receive multiple environmental parameters related to plant health status in the garden environment collected in real time by the sensor network; use a preset ecological health indicator system to analyze the multiple environmental parameters, dynamically evaluate the ecological environment quality of garden plants, and form a preliminary ecological health portrait; based on the preliminary ecological health portrait, combine historical ecological data and seasonal variation patterns to predict the ecological pressure trend of garden plants in future cycles; formulate and simulate multiple ecological intervention measures, and evaluate the ecological environment improvement effect under each ecological intervention measure; determine the optimal ecological intervention measure based on the evaluation results, and feed back the optimal ecological intervention measure to the garden management department to guide ecological maintenance actions.

[0290] 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. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0291] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0292] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0293] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module can be an output device, an input device, etc.

[0294] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0295] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0296] 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 an ecological assessment method for environmental monitoring of garden plants.

[0297] 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.

[0298] 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.

[0299] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0300] 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. An ecological assessment method for garden plant environmental monitoring, characterized in that: include: Receive multiple environmental parameters related to plant health status in the garden environment collected in real time by the sensor network; Analyze the multiple environmental parameters using a pre-set ecological health indicator system, dynamically evaluate the ecological environment quality of garden plants, and form a preliminary ecological health portrait; Based on the preliminary ecological health portrait, historical ecological data and seasonal variation patterns are combined to predict the ecological stress trend of garden plants in future cycles; According to the ecological pressure trend, formulate and simulate multiple ecological intervention measures, and evaluate the ecological environment improvement effect under each ecological intervention measure; Determine the best ecological intervention measures based on the evaluation results, and provide feedback to the garden management department to guide ecological maintenance actions; The method of analyzing the multiple environmental parameters using a preset ecological health indicator system, dynamically evaluating the ecological environment quality of garden plants, and forming a preliminary ecological health portrait includes: Mapping the collected multi-environmental parameters to corresponding ecological health indicators based on a preset ecological health indicator system, wherein the ecological health indicator system covers key environmental factors required for plant growth; Standardize the mapped ecological health indicators; Calculating a comprehensive score reflecting the ecological environment quality of garden plants based on the importance of the ecological health indicators and the interactive relationship between the ecological health indicators; Different ecological environment quality levels are divided according to the calculated comprehensive scores to construct a preliminary ecological health portrait of garden plants, which intuitively displays the health status and potential risk points of the environment in which the plants are located; The comprehensive score reflecting the ecological environment quality of garden plants is calculated based on the importance of the ecological health indicators and the interactive relationship between the ecological health indicators, including: Get each ecosystem health indicator The value of , and each of the ecological health indicators With corresponding importance weights ; The comprehensive score reflecting the ecological environment quality of garden plants is calculated using the following formula: : ； in, represents the number of ecological health indicators, Ecological health indicators The corresponding importance weights are, represents the actual measured value of the ecological health indicator, represents the summation expression, which represents the sum of the squares of all ecological health indicator weights and is used to standardize the comprehensive score. represents the standardization factor, which is used to prevent the comprehensive score from being distorted due to the excessive weight of ecological health indicators. represents the sum of weighted measurements of all ecological health indicators, represents the interaction between all ecological health indicators. Ecological health indicators and The correlation coefficient between The value range is ; in, , represents the number of observations, and They are Ecological health indicators in the observation and The observed value of and Ecological Health Indicators and The average value is calculated as follows: ; The above-mentioned preliminary ecological health portrait is combined with historical ecological data and seasonal variation patterns to predict the ecological pressure trend of garden plants in the future cycle, including: Obtain and organize the current ecological environment quality information reflected in the preliminary ecological health portrait, including key indicators of plant health status, environmental suitability, and pest and disease incidence; Collect and analyze long-term historical ecological data related to the garden plants, where the historical ecological data at least covers environmental parameter records, plant growth conditions, and the occurrence of pests and diseases in the past several growth cycles; Combined with the law of seasonal changes, identify the key ecological factors that affect the growth of garden plants, including bud temperature in spring, high temperature and drought in summer, leaf fall humidity in autumn, and low temperature and freezing damage in winter, so as to establish a seasonal ecological factor database; Using time series analysis methods, the historical ecological data are modeled to capture the temporal patterns and trends of ecological stress on garden plants; Combining the current ecological status information in the preliminary ecological health portrait, the seasonal ecological factor database, and the time series model of the historical ecological data, and using a machine learning algorithm to predict the ecological pressure trend faced by garden plants in future cycles; The method combines the current ecological status information in the preliminary ecological health portrait, the seasonal ecological factor database and the time series model of the historical ecological data, and uses a machine learning algorithm to predict the ecological pressure trend faced by garden plants in the future cycle, including: Extracting current ecological status information from the preliminary ecological health portrait , and obtain seasonal factors from the seasonal ecological factor database ; Using historical ecological data Building time series models; Based on trained machine learning models , combined with the current ecological status information ,Seasonal Factor , and time series of historical ecological data , predicting future ecological pressure ; The predicted ecological pressure is calculated using the following formula: : ； in, is the adjustment coefficient, is the historical ecological data at each time point The contribution of is a constant, is the average value of historical ecological data, is the error term.

2. The method according to claim 1, characterized in that According to the ecological pressure trend, a plurality of ecological intervention measures are formulated and simulated, and the ecological environment improvement effect under each ecological intervention measure is evaluated, including: Design multiple ecological intervention measures based on the predicted ecological pressure trends and the physiological characteristics and ecological needs of garden plants; For each ecological intervention measure, simulate the response changes of garden plants and their surrounding environment after the implementation of the measure; Based on the simulation results, the ecological environment improvement effect under each ecological intervention measure is evaluated.

3. The method according to claim 2, characterized in that The optimal ecological intervention measures are determined based on the evaluation results, and the optimal ecological intervention measures are fed back to the garden management department to guide ecological maintenance actions, including: A multi-objective optimization algorithm is used to comprehensively evaluate the simulation results, and one or more ecological intervention measures with the best ecological environment improvement effect are determined as the optimal solution; The optimal ecological intervention measures will be fed back to the garden management department in the form of electronic documents or reports to guide ecological maintenance actions.

4. An ecological assessment system for garden plant environment monitoring, characterized in that: include: A receiving module, used for receiving multiple environmental parameters related to plant health status in a garden environment collected in real time by a sensor network; An evaluation module, for analyzing the multiple environmental parameters using a preset ecological health indicator system, dynamically evaluating the ecological environment quality of garden plants, and forming a preliminary ecological health portrait; A prediction module is used to predict the ecological pressure trend of garden plants in the future cycle based on the preliminary ecological health portrait, combined with historical ecological data and seasonal change rules; Formulate a simulation module for formulating and simulating a plurality of ecological intervention measures according to the ecological pressure trend, and evaluate the ecological environment improvement effect under each ecological intervention measure; Feedback module, used to determine the optimal ecological intervention measures based on the evaluation results, and to feed back the optimal ecological intervention measures to the garden management department to guide ecological maintenance actions; The method of analyzing the multiple environmental parameters using a preset ecological health indicator system, dynamically evaluating the ecological environment quality of garden plants, and forming a preliminary ecological health portrait includes: Mapping the collected multi-environmental parameters to corresponding ecological health indicators based on a preset ecological health indicator system, wherein the ecological health indicator system covers key environmental factors required for plant growth; Standardize the mapped ecological health indicators; Calculating a comprehensive score reflecting the ecological environment quality of garden plants based on the importance of the ecological health indicators and the interactive relationship between the ecological health indicators; Different ecological environment quality levels are divided according to the calculated comprehensive scores to construct a preliminary ecological health portrait of garden plants, which intuitively displays the health status and potential risk points of the environment in which the plants are located; The comprehensive score reflecting the ecological environment quality of garden plants is calculated based on the importance of the ecological health indicators and the interactive relationship between the ecological health indicators, including: Get each ecosystem health indicator The value of , and each of the ecological health indicators With corresponding importance weights ; The comprehensive score reflecting the ecological environment quality of garden plants is calculated using the following formula: : ； in, represents the number of ecological health indicators, Ecological health indicators The corresponding importance weights are, represents the actual measured value of the ecological health indicator, represents the summation expression, which represents the sum of the squares of all ecological health indicator weights and is used to standardize the comprehensive score. represents the standardization factor, which is used to prevent the comprehensive score from being distorted due to the excessive weight of ecological health indicators. represents the sum of weighted measurements of all ecological health indicators, represents the interaction between all ecological health indicators. Ecological health indicators and The correlation coefficient between The value range is ; in, , represents the number of observations, and They are Ecological health indicators in the observation and The observed value of and Ecological Health Indicators and The average value is calculated as follows: ; The method according to claim 1 is characterized in that the prediction of the ecological pressure trend of garden plants in the future cycle based on the preliminary ecological health portrait combined with historical ecological data and seasonal variation patterns includes: Obtain and organize the current ecological environment quality information reflected in the preliminary ecological health portrait, including key indicators of plant health status, environmental suitability, and pest and disease incidence; Collect and analyze long-term historical ecological data related to the garden plants, where the historical ecological data at least covers environmental parameter records, plant growth conditions, and the occurrence of pests and diseases in the past several growth cycles; Combined with the law of seasonal changes, identify the key ecological factors that affect the growth of garden plants, including bud temperature in spring, high temperature and drought in summer, leaf fall humidity in autumn, and low temperature and freezing damage in winter, so as to establish a seasonal ecological factor database; Using time series analysis methods, the historical ecological data are modeled to capture the temporal patterns and trends of ecological stress on garden plants; Combining the current ecological status information in the preliminary ecological health portrait, the seasonal ecological factor database, and the time series model of the historical ecological data, and using a machine learning algorithm to predict the ecological pressure trend faced by garden plants in future cycles; The method combines the current ecological status information in the preliminary ecological health portrait, the seasonal ecological factor database and the time series model of the historical ecological data, and uses a machine learning algorithm to predict the ecological pressure trend faced by garden plants in the future cycle, including: Extracting current ecological status information from the preliminary ecological health portrait , and obtain seasonal factors from the seasonal ecological factor database ; Using historical ecological data Building time series models; Based on trained machine learning models , combined with the current ecological status information ,Seasonal Factor , and time series of historical ecological data , predicting future ecological pressure ; The predicted ecological pressure is calculated using the following formula: : ； in, is the adjustment coefficient, is the historical ecological data at each time point The contribution of is a constant, is the average value of historical ecological data, is the error term.

5. 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 an ecological assessment method for garden plant environment monitoring as described in any one of claims 1 to 3.

6. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, an ecological assessment method for garden plant environment monitoring as described in any one of claims 1 to 3 is implemented.

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