CN119006207B

CN119006207B - An ecological assessment method and system for garden plant environmental monitoring

Info

Publication number: CN119006207B
Application number: CN202411497490.0A
Authority: CN
Inventors: 李陵; 李艳; 李颖; 刘建军
Original assignee: Jingtianxia Ecological Environment Technology Co ltd
Current assignee: Jingtianxia Ecological Environment Technology Co ltd
Priority date: 2024-10-25
Filing date: 2024-10-25
Publication date: 2025-01-21
Anticipated expiration: 2044-10-25
Also published as: CN119006207A

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

Ecological assessment method and system for landscape plant environment monitoring

Technical Field

The embodiment of the application relates to the technical field of garden management, in particular to an ecological assessment method and system for monitoring the environment of garden plants.

Background

With the acceleration of the urban green space and landscape plants, the management and maintenance of urban green space and landscape plants becomes particularly important. The garden plants are not only important components of the urban ecological system, but also have irreplaceable effects on improving urban environment quality and resident life quality. However, the traditional garden plant management mode mainly depends on manual inspection and experience judgment, and comprehensive and real-time monitoring of plant growth environments is difficult to realize. Therefore, a technical scheme capable of monitoring the growth environment of garden plants in real time, dynamically evaluating the health condition of the plants and predicting the ecological pressure in the future is urgently needed to guide scientific ecological maintenance actions.

At present, the environmental monitoring of garden plants mainly depends on the following methods:

And (3) manual inspection, namely observing and recording plants regularly by garden management personnel, and knowing the growth condition and environmental condition of the plants. This method is time consuming and laborious and is greatly affected by personal experience and subjective judgment.

And the fixed monitoring station is used for setting an environment monitoring station at a specific place and collecting environmental parameters such as temperature, humidity, illumination and the like. Although more accurate data can be provided, the monitoring range is limited, and the whole garden area is difficult to cover.

The initial Internet of things technology is applied, part of gardens begin to try to acquire environmental data by using a sensor network, but the systems generally lack comprehensive ecological health assessment and prediction functions and are mainly used for simple data recording and alarming.

The existing scheme has the defects that:

The traditional manual inspection and fixed monitoring station method cannot realize comprehensive and real-time monitoring of the whole garden area, local environment changes are easy to miss, and problems are not found timely.

The comprehensive evaluation and prediction capability is lacking, and the existing sensor network application can collect a large amount of environmental data, but lacks an effective ecological health index system and a prediction model, so that the ecological environment quality of plants cannot be dynamically evaluated, and the future ecological pressure trend cannot be predicted.

The existing monitoring method and system mainly provide data recording and simple alarm functions, lack scientific ecological intervention measure making and evaluating mechanisms, and are difficult to effectively guide maintenance actions of garden management departments.

Disclosure of Invention

The embodiment of the application provides an ecological assessment method and system for garden plant environment monitoring, which are used for solving the problem of low garden management level in the prior art.

In a first aspect, an embodiment of the present application provides an ecological assessment method for monitoring an environment of a landscape plant, including:

Receiving multiple environmental parameters related to plant health conditions in a garden environment acquired in real time by a sensor network;

Analyzing the multiple environmental parameters by using a preset ecological health index system, dynamically evaluating the ecological environment quality of garden plants, and forming a preliminary ecological health portrait;

predicting the ecological pressure trend of the garden plants in a future period according to the preliminary ecological health portrait and by combining the historical ecological data and the seasonal variation law;

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;

And determining the optimal ecological intervention measures according to the evaluation results, and feeding back the optimal ecological intervention measures to a garden management department to guide ecological maintenance actions.

In a second aspect, an embodiment of the present application provides an ecological assessment system for monitoring an environment of a landscape plant, including:

The receiving module is used for receiving the multielement environmental parameters related to the plant health condition in the garden environment acquired by the sensor network in real time;

the evaluation module is used for analyzing the multiple environmental parameters by utilizing a preset ecological health index system, dynamically evaluating the ecological environment quality of the garden plants and forming a preliminary ecological health portrait;

The prediction module is used for predicting the ecological pressure trend of the garden plant in a future period according to the preliminary ecological health portrait and by combining the historical ecological data and the seasonal variation law;

The simulation module is used for making and simulating a plurality of ecological intervention measures according to the ecological pressure trend and evaluating the ecological environment improvement effect under each ecological intervention measure;

And the feedback module is used for determining the optimal ecological intervention measures according to the evaluation results and feeding back the optimal ecological intervention measures to the garden management department to guide ecological maintenance actions.

In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component, where the storage component stores one or more computer instructions, and the one or more computer instructions are used to be invoked and executed by the processing component to implement an ecological assessment method for monitoring an environment of a garden plant according to any one of the first aspect.

In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, where the computer program when executed by a computer implements an ecological assessment method for monitoring a landscape plant environment according to any one of the first aspect.

In the embodiment of the application, multiple environmental parameters related to plant health conditions in a garden environment are received, which are acquired by a sensor network in real time, are analyzed by utilizing a preset ecological health index system, the ecological environment quality of the garden plant is dynamically evaluated to form a preliminary ecological health portrait, the ecological pressure trend of the garden plant in a future period is predicted according to the preliminary ecological health portrait and by combining historical ecological data and seasonal variation rules, a plurality of ecological intervention measures are formulated and simulated, the ecological environment improvement effect under each ecological intervention measure is evaluated, the optimal ecological intervention measure is determined according to the evaluation result, and the optimal ecological intervention measure is fed back to a garden management department to guide ecological maintenance actions. The technical scheme provided by the application can effectively improve the management level of garden plants and promote ecological environment protection and social sustainable development in a larger range.

These and other aspects of the application will be more readily apparent from the following description of the embodiments.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart of an ecological assessment method for landscape plant environment monitoring according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of an ecological assessment system for monitoring environment of garden plants according to an embodiment of the present application;

Fig. 3 is a schematic structural diagram of a computing device according to an embodiment of the present application.

Detailed Description

In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present application with reference to the accompanying drawings.

In some of the flows described in the specification and claims of the present application and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.

Fig. 1 is a flowchart of an ecological assessment method for monitoring the environment of garden plants according to an embodiment of the present application, as shown in fig. 1, the method includes:

101. Receiving multiple environmental parameters related to plant health conditions in a garden environment acquired in real time by a sensor network;

this step involves the real-time collection of various environmental parameters closely related to plant health through a network of sensors deployed in gardens. These data are the basis for subsequent analysis and evaluation and are critical for dynamically monitoring plant growth environment, timely finding potential problems and developing scientific management measures.

The sensor network is a network system formed by a plurality of sensor nodes, and the nodes can work cooperatively to continuously collect and transmit environmental data.

And the real-time acquisition means that the sensor network continuously collects data and immediately transmits the data to a central processing system or a cloud server so as to realize instant data analysis and processing.

The multiple environmental parameters comprise a series of environmental factors which influence plant health, such as temperature, humidity, illumination intensity, soil moisture, soil pH value, carbon dioxide concentration in the air and the like.

Plant health refers to the overall health level that plants exhibit during their growth, including growth rate, leaf color, pest and disease conditions, and the like.

A plurality of sensor nodes are arranged in gardens to ensure critical positions covering the entire area. For example around different types of plants, near irrigation systems, important landscape nodes, etc.

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, wind speed and direction sensors.

And setting the data acquisition frequency according to specific requirements. Typically, data can be collected every 10 minutes to every hour, ensuring timeliness and accuracy of the data.

And the data transmission step of transmitting the acquired data to a central processing system or a cloud server by the sensor node through a wireless communication technology (such as Zigbee, loRa, wi-Fi). In the data transmission process, the safety and the integrity of the data are required to be ensured, and the data are prevented from being lost or tampered.

And storing the acquired data on a central processing system or a cloud server. The data storage should be structured to facilitate subsequent data processing and analysis. The stored data should be accompanied by time stamps and location information to facilitate tracking and analysis of environmental changes at a particular time and place.

And data preprocessing, namely performing preliminary preprocessing on the acquired data, including removing noise, filling missing values, smoothing the data and the like, so as to improve the data quality. The data preprocessing is beneficial to the subsequent analysis and modeling process, and the reliability and accuracy of the data are ensured.

It is assumed that a park management department wishes to monitor the plant growth environment in a park in real time. The following steps are specific:

Sensor nodes are installed at selected key positions in the park, and each node comprises temperature, humidity, illumination intensity, soil moisture and soil pH value sensors.

The sensor nodes are connected to the central processing system through LoRa wireless communication technology.

Data are collected every 15 minutes, and the real-time performance and accuracy of the data are ensured.

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

And after the central processing system receives the data, the data is immediately stored and preliminarily processed.

The collected data is stored in a central database, and each record contains a time stamp, location information, and various environmental parameter values.

The database adopts a structural design, so that subsequent data query and analysis are facilitated.

And carrying out preliminary pretreatment on the acquired data, removing abnormal values and noise, and filling in missing values.

The results after data preprocessing are used for subsequent ecological health assessment and prediction.

The method has the beneficial effects that the real-time data acquisition reduces the workload of manual inspection and improves the monitoring efficiency by acquiring the multielement environmental parameters related to the plant health condition in the garden environment in real time. The high-frequency data acquisition can accurately reflect the change of environmental parameters, and a reliable data basis is provided for subsequent analysis. By means of real-time data monitoring, environment parameter abnormality can be found rapidly, countermeasures can be taken timely, and plant health is guaranteed. Based on the analysis result of the real-time data, the garden management department can make more scientific and accurate management decisions, optimize the resource allocation and improve the management level.

In summary, the step is the basis of the whole ecological assessment method, and provides solid data support for subsequent dynamic assessment, prediction and intervention by collecting multiple environmental parameters in real time.

102. Analyzing the multiple environmental parameters by using a preset ecological health index system, dynamically evaluating the ecological environment quality of garden plants, and forming a preliminary ecological health portrait;

The ecological health index system refers to a set of scientifically verified standard system for evaluating the quality of the environment where garden plants are located. The system typically includes a series of key indicators such as temperature, humidity, light intensity, soil pH, soil humidity, air quality, etc.

Dynamic evaluation, namely continuously updating an evaluation result of ecological environment quality according to environmental parameters acquired in real time and combining historical data and current season characteristics.

The primary ecological health portrait refers to an overview reflecting the current ecological environment quality of garden plants and comprising health status, potential risk points and the like by comprehensively analyzing multiple environmental parameters and ecological health indexes.

In a specific embodiment, the above-described large public garden of a certain public park authority is continued as an example. The garden has deployed a sensor network and is collecting environmental parameters in real time. Next, it will be shown how to analyze these data using a preset ecological health index system and dynamically evaluate the ecological environment quality of the landscape plants, ultimately forming a preliminary ecological health portrait.

Setting a series of ecological health indicators, including:

The temperature is 15-30 ℃ in a proper range;

The humidity is 40% -80% in the proper range;

Illumination intensity, namely setting a proper illumination intensity range according to different plant types;

the pH value of the soil is 6.0-7.5;

the soil humidity is 20% -40% in a proper range;

the Air Quality Index (AQI) is suitably in the range of 0 to 50 (good).

Mapping the multi-element environment parameters acquired in real time to the ecological health indexes, and performing standardized treatment to enable the multi-element environment parameters to meet the unified evaluation standard. For example, if the soil humidity is 35%, it is considered to be in a suitable range, the score is 1, and if it is 15%, it is considered to be too low, the score is 0.

And calculating the comprehensive score reflecting the ecological environment quality according to the importance of each index and the interaction relation among the indexes. For example, assume that the importance weight of temperature is 0.2, humidity is 0.3, illumination intensity is 0.1, soil pH is 0.2, soil humidity is 0.1, and air quality index is 0.1.

According to comprehensive scoringThe ecological quality is divided into several classes, for example:

0.8 or more, excellent;

0.6 to 0.8 percent;

0.4 to 0.6 percent of general;

Less than 0.4, worse.

By the grading mode, the quality of the current ecological environment can be intuitively known.

And forming a preliminary ecological health portrait according to the evaluation result, and displaying the current ecological environment quality level, the state of each key index and potential risk points.

For example, assuming a composite score of 0.7, the primary ecological health image shows an ecological environment quality of "good", specifically, temperature, humidity, and soil pH are all in the proper ranges, but the soil humidity is slightly lower and the air quality index is good.

The present application contemplates that while a variety of environmental parameters can be collected, there is often a lack of in-depth analysis and comprehensive assessment of such data. In order to solve the problems, an alternative scheme is provided in the embodiment of the application, the mapping and standardization processing are carried out on the multi-element environment parameters through a preset ecological health index system, and the comprehensive score is calculated by combining the importance of each index and the interaction relation of each index, so that a preliminary ecological health image is constructed. Therefore, the ecological environment quality of the garden plants can be estimated more comprehensively and accurately, visual results are provided, and the garden management departments are helped to make scientific decisions.

Optionally, in step 102, "analyzing the multiple environmental parameters by using a preset ecological health index system, dynamically evaluating the ecological environmental quality of the garden plants and forming a preliminary ecological health portrait", the method comprises mapping the collected multiple environmental parameters onto corresponding ecological health indexes based on the preset ecological health index system, wherein the ecological health index system covers key environmental factors required by plant growth, carrying out standardization processing on the mapped ecological health indexes, calculating comprehensive scores reflecting the ecological environmental quality of the garden plants according to the importance of the ecological health indexes and the interaction relationship between the ecological health indexes, and dividing different ecological environmental quality grades according to the calculated comprehensive scores to construct the preliminary ecological health portrait of the garden plants, wherein the ecological health portrait intuitively displays the health state and potential risk point of the environment where the plants are located.

The ecological health index system is a set of scientifically verified standard system for evaluating the quality of the environment where the garden plants are located. Mapping, namely converting the collected environmental parameters into values corresponding to the ecological health indexes. And (3) standardized processing, namely converting the data with different dimensions into uniform standard dimensions, so that the subsequent analysis is convenient. Importance weight, namely, the quantitative representation of the influence degree of each ecological health index on the overall ecological environment quality. Interaction relationship between different physiological health indicators. And (3) comprehensive scoring, namely calculating a comprehensive evaluation value based on each ecological health index and weight thereof. Ecological environment quality level, different ecological environment quality levels according to comprehensive grading. The primary ecological health image is a chart or report for intuitively displaying the environment health state and potential risk points of the plant.

In the embodiment of the application, for example, the multi-element environment parameters (such as temperature, humidity, illumination intensity and the like) acquired in real time are mapped to the preset ecological health index. For example, the temperature data is mapped to an index of "suitable temperature range". And carrying out standardization treatment on the mapped ecological health indexes to ensure that all indexes are in the same scale. For example, the data of temperature, humidity, etc. are all converted into normalized values between 0 and 1. Based on the calculated composite scoreDifferent ecological environment quality levels are divided. For example, it is excellent in 0.8 or more, preferably 0.6 to 0.8, more preferably 0.4 to 0.6, and still more preferably 0.4 or less. Based on the evaluation result, a preliminary ecological health portrait is formed, and the current ecological environment quality level, the state of each key index and the potential risk point are displayed.

And (3) assuming that a sensor network is deployed in a greening area in a park, and acquiring environmental parameters in real time. The following steps are specific:

The data collected by the sensor are as follows:

Temperature of 28 DEG C

Humidity of 70%

The illumination intensity is 1500lux

The pH value of the soil is 6.5

Soil moisture of 30%

Air Quality Index (AQI) 45

The normalized data are as follows:

temperature 0.6 (assuming a suitable range of 25-30 ℃ C.)

Humidity of 0.75

Illumination intensity 0.75 (assuming a suitable range of 1000-2000 lux)

Soil pH 0.75

Soil moisture 0.75

Air quality index 0.9 (assuming a suitable range of 0-50)

The importance weights of the indexes are assumed to be respectively:

temperature of 0.2

Humidity of 0.3

The illumination intensity is 0.1

Soil pH 0.2

Soil moisture 0.1

Air quality index 0.1

Correlation coefficient matrixThe following are provided:

;

Substituting formula to calculate comprehensive score :

;

The composite score is 1.6635, which belongs to the "good" grade.

The primary ecological health portrait shows that the ecological environment quality of the greening area is excellent, and all indexes are in a proper range without obvious potential risk points.

By the method, the ecological environment quality of the garden plants can be comprehensively and accurately estimated, visual ecological health images can be provided, and a garden management department can be helped to find and solve potential problems in time. In addition, the method can also improve the management efficiency, reduce the workload of manual inspection and realize intelligent and fine garden management.

103. Predicting the ecological pressure trend of the garden plants in a future period according to the preliminary ecological health portrait and by combining the historical ecological data and the seasonal variation law;

this step aims at predicting the ecological pressure likely to be faced by the garden plants in a future period of time by using the constructed preliminary ecological health portrait, the historical ecological data and the seasonal variation rules. By such predictions, provision can be made in advance, and corresponding management measures can be taken to mitigate adverse effects.

And (5) the primary ecological health portrait is a comprehensive evaluation result reflecting the quality of the current ecological environment, which is formed by the previous step, and comprises the state of environmental parameters, comprehensive scores and potential risk points.

Historical ecological data refers to environmental parameter data collected in a past period of time, and the data records the change condition of plant growth environment, including temperature, humidity, illumination intensity and the like.

Seasonal variation law refers to the law of influence of different seasons in one year on plant growth environment, for example, proper temperature is required in the germination period in spring, water supply can be influenced by high-temperature drought in summer, soil humidity is required to be paid attention in the defoliation period in autumn, and plant survival can be influenced by low-temperature freezing injury in winter.

The ecological pressure trend refers to the change trend of ecological environment pressure faced by plants in a period of time in the future, such as pest and disease damage risk, drought, flood and the like.

The state information of the current plant and the environment thereof is extracted from the preliminary ecological health portrait, including but not limited to key indexes such as plant health state, environment suitability, pest and disease occurrence rate and the like.

And collecting and arranging environmental parameter records (such as temperature, humidity and illumination), plant growth conditions, plant pest occurrence and the like in the past several growth periods.

These data are analyzed to identify long-term trends and periodic patterns of change.

According to the seasonal variation law, key ecological factors of each season are identified, such as germination temperature in spring, high-temperature drought in summer, leaf falling humidity in autumn and low-temperature freeze injury in winter.

A database containing these seasonal ecological factors is built for later use.

And modeling the historical ecological data by using a time sequence analysis method, and capturing the time mode and trend of the plant ecological pressure.

This may be achieved by a statistical model (e.g., ARIMA) or a machine learning model (e.g., LSTM).

And combining the current ecological state information, the seasonal ecological factor database and the time sequence model of the historical ecological data.

Machine learning algorithms (e.g., random forests, support vector machines, etc.) or other predictive models are used to predict the ecological pressure trends that plants may face in future cycles.

In the embodiment of the application, the cherry blossom tree group in a park is assumed to experience drought and pest problems with different degrees in the past few years. In order to better predict future ecological pressures, park authorities take the following steps:

Based on the latest sensor data and ecological health portrait, it is known that the current soil humidity is low and partial trees have slight signs of diseases and insect pests. Reviewing the growth data of the cherry blossom trees in the past five years, the drought of a certain degree is found in summer every year, and serious pest and disease damage outbreaks occur once every two years or so. The minimum temperature required by germination in spring is 10 ℃, the soil humidity is required to be kept above 30% in high-temperature drought in summer, proper humidity is required to prevent fungal infection in the defoliation period in autumn, and low-temperature freeze injury is required to be prevented in winter. Historical temperature and humidity data were modeled using the ARIMA model and it was found that there were significant drought years every three years. In combination with the current ecological state, seasonal ecological factors, and the results of the ARIMA model, a random forest algorithm is used to predict that severe drought may occur again in the next two years, and the risk of pest outbreaks is predicted for the next year. The coping strategy can be formulated in advance by predicting the future ecological pressure, so that the loss caused by the emergency is reduced. And management work such as irrigation, fertilization, pest control and the like is reasonably arranged, so that the resource utilization efficiency is improved. The decision made based on the data analysis and the prediction results is more scientific and reliable, and is helpful for improving the overall level of garden management.

Through this step, the garden manager can better foresee future challenges and be prepared in advance, ensuring that the plants can grow in an optimal state.

The present application contemplates the lack of predictive capability for future ecological pressure trends, although current environmental parameters can be collected and analyzed in real time. In order to solve the above problems, an alternative scheme is provided in the embodiment of the present application, in which a time sequence analysis and a machine learning algorithm are adopted to predict the ecological pressure trend in a future period by combining the preliminary ecological health portrait, the long-term historical ecological data and the seasonal variation law. Therefore, the accuracy of prediction can be improved, a garden management department can be helped to plan future management strategies better, and adverse effects caused by environmental changes are reduced.

Optionally, the step 103 of predicting the ecological pressure trend of the garden plants in the future period according to the preliminary ecological health image and combining the historical ecological data and the seasonal variation law comprises the steps of acquiring and sorting the current ecological environment quality information reflected in the preliminary ecological health image, including plant health status, environment suitability and plant diseases and insect pests occurrence rate key indexes, collecting and analyzing long-term historical ecological data related to the garden plants, wherein the historical ecological data at least comprise environmental parameter records, plant growth conditions and plant diseases and insect pests occurrence conditions in a plurality of growth periods in the past, combining the seasonal variation law, identifying key ecological factors influencing the growth of the garden plants, including germination temperature in spring, high-temperature drought in summer, leaf humidity in autumn and low-temperature freezing injury in winter, so as to establish a seasonal ecological factor database, modeling the historical ecological data by using a time sequence analysis method so as to capture the time mode and trend of the ecological pressure of the garden plants, and predicting ecological factors of the garden plants in the future period by combining the seasonal ecological factor database and the time sequence prediction model of the future ecological pressure trend of the garden plants.

Seasonal ecological factors refer to key environmental factors which have obvious influence on plant growth in different seasons, such as spring germination temperature, summer high-temperature drought, autumn defoliation humidity, winter low-temperature freeze injury and the like.

Time series analysis, a statistical method for analyzing data over time and capturing patterns and trends thereof.

Machine learning algorithms, a class of algorithms that automatically learn and refine predictive models through training data, such as random forests, support vector machines, and the like.

And extracting key indexes related to plant health states (such as leaf color and branch development), environment suitability (such as illumination intensity and air quality), plant disease and insect pest occurrence rate and the like from the preliminary ecological health portrait.

Collecting historical ecological data at least covering a plurality of complete growth cycles, including but not limited to daily/monthly average air temperature, rainfall, wind speed and other environmental parameters, and recording the specific growth performance of the contemporaneous plants and any known pest and disease events.

Analyzing the influence characteristics of different seasons on plant growth, determining important factors such as the minimum temperature required by the germination period, ideal soil humidity level during summer drought period and the like, and constructing a special seasonal ecological factor library according to the important factors.

A suitable time series analysis tool (e.g. ARIMA model) is applied to train the model based on the historical data in the second step in order to capture the pattern of the development of ecological stress faced by the plant over time.

The latest ecological condition obtained in the first step, the seasonal factor knowledge base formed in the third step and the time sequence model obtained in the fourth step are combined together, and a proper machine learning technology (such as random forest) is used for predicting the possible pressure condition in a plurality of growth periods in the future.

In the embodiment of the application, it is assumed that a city park wishes to improve the maintenance of the internal cherry blossom tree group. Firstly, the manager can know that the tree is generally in good condition at present according to the latest ecological health check result, but has slight aphid attack signs. Next, they reviewed the relevant data over the last decade, noting that there was a more serious aphid outbreak every approximately four years. In addition, the problems of leaf fall and the like caused by low temperature delay flowering in spring and extreme high temperature in summer are also found. Based on these observations, the team defines a series of key ecological factors and analyzes the trend of temperature and precipitation using the ARIMA model. Finally, the Support Vector Machine (SVM) algorithm is adopted to integrate all available information, so that the main ecological challenges possibly occurring in the next three years are successfully predicted, the large-scale aphid invasion is predicted to occur again after two years, and meanwhile, attention is paid to the prevention of hot weather possibly occurring in summer of the next year. This prediction allows the park to take steps ahead of time to enhance precautions such as adjusting irrigation plans, preparing sufficient pesticide reserves, etc., thus effectively reducing the actual extent of damage.

By implementing the scheme, a garden manager can more accurately predict the problems possibly occurring in the future, optimize the resource allocation according to the prediction result and improve the working efficiency. This not only helps to maintain the optimal growth state of the plant, but also reduces the additional expense caused by the emergency situation, representing significant advantages brought by data driven decisions.

104. 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;

According to the prediction results of ecological pressures (such as temperature, humidity, plant diseases and insect pests and the like) possibly faced by the garden plants in the future period, a plurality of possible intervention measures are designed by combining the physiological characteristics and ecological requirements of the plants. Such measures may include, but are not limited to, irrigation strategy adjustments, fertilization plan optimization, shade facility settings, pest control methods, and the like.

For each designed ecological intervention, a model or simulation software is used to simulate the change in the plant and its surrounding environment after the implementation of the action. Such simulation can help understand how specific interventions affect key ecological parameters and predict changes in plant health status and growth conditions.

Based on the results of the simulation, the ecological environment improvement effect under each intervention is evaluated. This generally involves comparing the expected improvements in plant growth conditions under different measures, such as changes in water availability, soil nutrient levels, pest and disease incidence, etc.

Other factors such as economic cost, implementation difficulty, sustainability, etc. can also be considered in the evaluation to ensure that the selected measure is not only effective but also viable.

And comprehensively evaluating all simulation results by utilizing a multi-objective optimization algorithm, and finding out one or more intervention measures with the best comprehensive benefits (namely, the best effective relief of ecological pressure and the satisfaction of other constraint conditions) as the optimal scheme recommended to the garden management department.

Through such a process, scientific basis can be provided for garden management, and a decision maker is helped to make a more reasonable ecological maintenance decision, so that healthy growth of garden plants is promoted, and the ecological environment quality of the whole garden area is improved.

The present application contemplates that experience-based management and intervention is commonly employed. This approach often lacks accurate insight into dynamic changes in the ecological environment, as well as efficient predictions of future ecological pressures. Therefore, when facing the complex situations of climate change, outbreak of plant diseases and insect pests, the traditional method can not effectively cope with the complex situations in time, so that the resource waste or the ecological restoration effect is poor. In order to solve the above problems, the embodiments of the present application provide a further alternative scheme, which aims to make more effective and economical ecological intervention measures through scientific analysis and simulation so as to improve the quality of the growing environment of the garden plants.

Optionally, the step 104 of establishing and simulating a plurality of ecological intervention measures according to the ecological pressure trend and evaluating the ecological environment improvement effect under each ecological intervention measure includes designing a plurality of ecological intervention measures according to the predicted ecological pressure trend and combining physiological characteristics and ecological requirements of garden plants, simulating response changes of the garden plants and surrounding environments of the garden plants after the measures are implemented for each ecological intervention measure, and evaluating the ecological environment improvement effect under each ecological intervention measure based on simulation results.

Ecological stress trend refers to the trend of change of adverse conditions on plant survival caused by natural factors (such as climate) or artificial activities.

Physiological properties refer to the inherent biological properties of a plant in response to changes in the external environment.

Ecological requirements-the proper environmental conditions required by plants in order to maintain normal growth and development.

Ecological intervention means manual means such as irrigation, fertilization and the like, which are adopted for improving the plant growth environment.

In response to the change, the plant and its surrounding environment state change after the ecological intervention is implemented.

The ecological environment improving effect refers to the actual influence degree of the ecological intervention measures on the improvement of the health condition of plants and the quality of surrounding environment.

Designing a plurality of ecological intervention measures, namely providing a series of possible intervention measures according to the prediction result of future ecological pressure and combining the physiological characteristics and ecological requirements of specific plants. For example, if it is predicted that there will be severe drought in summer, it may be considered to increase the irrigation frequency or to construct a drip irrigation system, and if it is predicted that there is a high humidity in autumn, which may cause fungal diseases, it is necessary to prepare a bactericide spray schedule in advance.

Simulating response variation, namely simulating specific influence of each intervention measure after implementation by using a computer model. This step may involve predicting moisture distribution using a hydrologic model, estimating soil nutrient level changes using a nutrient transport model, and the like.

And (3) evaluating the improvement effect, namely analyzing the data obtained by simulation and evaluating the effect of each intervention measure. This includes, but is not limited to, improvements in plant survival, growth rate, probability of occurrence of insect pests, and the like.

In the embodiment of the application, a lot of precious tree species are planted in a park, and the situation that the area possibly experiences continuous high-temperature drought in the next few years is found through data analysis, so that the method forms a potential threat to the tree. Based on this information:

Two main intervention strategies are provided, namely, installation of an intelligent drip irrigation system and addition of a sunshade net.

Simulation response change, namely, simulation results of the intelligent drip irrigation system show that the intelligent drip irrigation system can effectively keep the soil wettability within a proper range and reduce water resource waste. The sunshade net can obviously reduce the surface temperature and protect trees from being influenced by strong direct sunlight.

The comprehensive benefits of the two measures are compared, and the intelligent drip irrigation system is found that the intelligent drip irrigation system can save more water resources in the long term and is more beneficial to tree growth although the initial investment is larger.

Through the process, not only the scientificity and the accuracy of the ecological management decision are improved, but also the selected measures are ensured to be efficient and sustainable. The direct benefit of this is that unnecessary expenses are reduced, and simultaneously, good growth state of garden plants is guaranteed to the greatest extent, and problems caused by lack of accurate prediction and evaluation in the conventional method are solved.

105. And determining the optimal ecological intervention measures according to the evaluation results, and feeding back the optimal ecological intervention measures to a garden management department to guide ecological maintenance actions.

This step aims at selecting the most effective interventions based on the evaluation of the effect of the different ecological interventions in the previous step, and feeding back these to the garden management so that they can take corresponding actions to optimize the plant growth environment.

Evaluation results refer to effect evaluation of different physiological interventions obtained through simulation and analysis.

And (3) the optimal ecological intervention measures are optimal schemes selected by comprehensively considering the factors such as effects, cost, implementation difficulty and the like in all the evaluated measures.

Feedback that the selected optimal measure is provided in report or other form to the department or person responsible for managing the garden.

Ecological maintenance actions, namely specific management and maintenance activities adopted by a garden management department according to feedback, such as adjusting irrigation plans, fertilization strategies and the like.

Comprehensive evaluation, namely comprehensively evaluating each simulated ecological intervention measure, wherein the comprehensive evaluation comprises various factors such as the effect of the ecological intervention measure on ecological environment improvement, economic cost, implementation feasibility and the like.

Multi-objective optimization, namely weighing various factors by adopting a multi-objective optimization algorithm (such as genetic algorithm, particle swarm optimization and the like), and determining one or more ecological intervention measures with the best comprehensive benefit as an optimal scheme.

Detailed solutions are formulated for the selected optimal measures, detailed embodiments are formulated, including specific operational steps, required resources, scheduling, and expected effects.

And (3) writing a feedback report, namely arranging the selected optimal measure and the implementation scheme thereof into a written report, wherein the report contains background information, an evaluation method, a simulation result, recommended measures, implementation details and the like.

Communication and feedback, namely communication with garden management departments, introduction of report content and solution of related problems. Ensuring that the administrator adequately understands and agrees to the proposed measures.

And executing and supervising, namely assisting or supervising the implementation of measures after the proposal is adopted by a garden management department, ensuring the implementation according to a preset plan and timely adjusting the proposal to cope with the change in actual operation.

In the embodiment of the application, the urban park is supposed to take measures to recover the damaged lawn area after the urban park is subjected to summer high-temperature drought. Through early data collection, simulation and evaluation, the following specific implementation steps are as follows:

three possible interventions were evaluated, increasing irrigation frequency, using water retention agents, laying sunshade nets.

Simulation results show that the water retention capacity of soil can be effectively improved by using the water retention agent, the evaporation loss is reduced, and meanwhile, the cost is moderate and the implementation is easy.

Through a multi-objective optimization algorithm, factors such as cost, water resource saving effect, construction difficulty and the like are comprehensively considered, and finally, a water-retaining agent is selected as an optimal scheme.

A detailed water-retaining agent application scheme is formulated, including application amount, application method, application time and the like, and the soil humidity after application is expected to be improved by 20%.

A detailed report is written, including background introduction, evaluation process, simulation results, recommended measures, and specific implementation steps.

Meeting with park management department introduces report content and answers questions about water-retaining agent type selection and application technology.

Park authorities adopted advice to begin purchasing the water retention agent and arrange for application. The project team periodically checks the effect of the application and fine-tunes according to the actual situation.

Through this series of steps, the garden management department can not only obtain the support of scientific basis, but also constantly learn and improve in practice, thereby better maintaining and managing the garden environment.

The present application contemplates that the methods currently commonly employed in conventional schemes are based on a single objective optimization or rely on expert experience. In order to solve the problems, the embodiment of the application provides an alternative scheme for comprehensively evaluating the simulation result by using a multi-objective optimization algorithm and feeding back the optimal ecological intervention measure to a garden management department in the form of an electronic document or report. By the method, more comprehensive, scientific and systematic ecological maintenance decision can be realized, so that the efficiency and effect of garden management are improved.

Optionally, the step 105 of determining the optimal ecological intervention measures according to the evaluation results and feeding back the optimal ecological intervention measures to the garden management department to guide ecological maintenance actions includes comprehensively evaluating the simulation results by adopting a multi-objective optimization algorithm, determining one or more ecological intervention measures with the optimal ecological environment improvement effect as an optimal scheme, and feeding back the optimal ecological intervention measures to the garden management department in the form of electronic documents or reports to guide ecological maintenance actions.

The multi-objective optimization algorithm is a calculation method capable of simultaneously considering a plurality of objective functions (such as cost, effect, implementation difficulty and the like) and searching an optimal solution set, and is commonly known as a multi-objective genetic algorithm, NSGA-II and the like.

Comprehensive evaluation refers to the process of comprehensively evaluating different ecological interventions from multiple dimensions.

Electronic documents or reports, the selected optimal ecological intervention measures are arranged into a format which is convenient to read and understand so as to be referred to and executed by a garden management department.

Multi-objective optimization algorithm all simulated ecological interventions are evaluated using a multi-objective optimization algorithm (e.g. NSGA-II). The algorithm is able to find a balance point among multiple conflicting targets, generating a series of non-inferior solutions (Pareto fronts). And then selecting one or more optimal schemes according to actual conditions.

And (3) selecting one or more ecological intervention measures with the best ecological environment improvement effect from a non-inferior solution set obtained by the multi-objective optimization algorithm by combining with actual demands (such as budget limit, resource availability and the like) as a final recommended scheme.

Electronic document or report, the selected optimal ecological intervention and detailed implementation thereof are arranged into the form of electronic document or report. The report should include background presentation, evaluation process, simulation results, recommended action, specific implementation details, etc.

And feeding back, namely submitting the compiled electronic document or report to a garden management department, and carrying out necessary communication and explanation to ensure that the management department fully understands and accepts the proposed measures.

In the embodiment of the application, supposing that a certain city park faces the problem of high temperature drought in summer, measures are needed to be taken to protect precious trees. Through early data collection, simulation and evaluation, the following specific implementation steps are as follows:

Four possible interventions were evaluated using NSGA-II algorithm, increasing irrigation frequency, using water retention agents, laying sunshade nets, adjusting tree species layout. Simulation results show that each measure has advantages and disadvantages in terms of cost, water resource saving effect, construction difficulty and the like. NSGA-II generates a Pareto front, showing a series of non-inferior solutions. Two measures are selected as the optimal scheme by combining the actual budget and resource conditions of the park, namely, increasing irrigation frequency and using a water-retaining agent. These two measures are well balanced in terms of cost and effect.

A detailed report was written, the contents including:

The background introduction is the influence of high temperature drought in summer on trees.

And (3) evaluating the application and simulation results of the multi-objective optimization algorithm.

Measures are recommended to increase the irrigation frequency and to use water retention agents.

Specific implementation details are the specific arrangement of irrigation frequency, the application amount of the water-retaining agent and the application method.

Feedback to the garden management department:

the report is sent to park management and the meeting is held for detailed explanation. The meeting answers questions about technical details of irrigation frequency adjustment, selection criteria for water retention agent, etc.

According to the embodiment of the application, the optimal balance point can be found among a plurality of mutually conflicting targets through the multi-target optimization algorithm, so that the limitation of single-target optimization is avoided. Detailed electronic documents or reports are provided to ensure that the authorities have a clear understanding of recommended actions and their implementation details. By selecting the optimal intervention measures, the limited resources can be utilized more effectively, and the economic benefit is maximized. Through a multi-objective optimization algorithm, an optimal balance point can be found among a plurality of mutually conflicting objectives, and the limitation of single objective optimization is avoided. Based on the analysis of the data and the model, 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 condition of garden plants can be continuously improved.

Through this series of steps, the garden management department can not only obtain the support of scientific basis, but also constantly learn and improve in practice, thereby better maintaining and managing the garden environment. This not only improves the quality of plant growth, but also promotes the ecological environment of the entire garden area.

The application considers that in the existing garden plant ecological health evaluation method, a simple weighted average or single index evaluation method is generally adopted. In order to solve the problems, the application provides a method for calculating comprehensive scores based on importance of ecological health indexes and correlations thereof. By the method, the ecological environment quality of the garden plants can be comprehensively and scientifically estimated, so that more accurate management advice is provided.

Optionally, the calculating the comprehensive score reflecting the ecological environment quality of the garden plants according to the importance of the ecological health indexes and the interaction relation among the ecological health indexes comprises the steps of obtaining each ecological health indexWhereinAnd each of the ecological health indicatorsWith corresponding importance weights;

Calculating a comprehensive score reflecting the ecological environment quality of garden plants using the following formula:

;

Wherein, The number of the ecological health indicators is indicated,Indicating an ecological health indexThe corresponding importance weight is used to determine the importance of the object,Representing the actual measured value of the ecological health index,Representing a summation expression, representing the sum of squares of all ecological health indicator weights, for normalizing the composite score,Represents a normalization factor for preventing the distortion of the composite score due to the excessive weight of the ecological health index,Representing the sum of weighted measurements of all ecological health indicators,Representing the interactive relation between every two ecological health indexes,Indicating an ecological health indexAndCorrelation coefficient betweenThe value range of (2) is;

Wherein, ,The number of observations is indicated and,AndRespectively the firstEcological health index in secondary observationAndIs used for the observation of the (a),AndRespectively are ecological health indexesAndIs calculated as follows:。

Ecological health index [ ] ) Specific parameters for measuring the ecological environment quality of garden plants, such as temperature, humidity, illumination intensity and the like are specified.

Importance weight [ ]) Representing the importance degree of each ecological health index on the overall ecological environment quality.

Correlation coefficient [ ]) The linear relation strength between two ecological health indexes is measured, the value range is [ -1,1], wherein 1 represents complete positive correlation, -1 represents complete negative correlation, and 0 represents no correlation.

Normalized factor [ ]) An adjustment factor for preventing the distortion of the comprehensive score caused by the excessive weight of the ecological health index.

Weighted measurement sum [ ]) The sum of products of all ecological health indexes and the corresponding weights.

Interaction relation item) The effect of interactions between different physiological health indicators is taken into account.

In the embodiment of the application, it is assumed that an urban park needs to evaluate the ecological environment quality of the sakura group. The following steps are specific:

Three main ecological health indexes, namely temperature, are determined ) Humidity%) The pH value of the soil)。

Giving weight to temperature=0.5, Humidity=0.3, Soil pH=0.2。

Measured value of temperature=25 ℃, Humidity=70%, Soil pH value=6.5。

Assuming that multiple observations have been collected, the correlation coefficients are calculated:

=0.8 (positive correlation between temperature and humidity)

= -0.5 (Temperature inversely related to soil pH)

=0.2 (Humidity and soil pH weak positive correlation)

According to the formula:

;

The calculation process comprises the following steps:

normalization factor ;

Sum of weighted measurements;

Interactive relationship item ;

;

The comprehensive score is 56.335, which indicates that the ecological environment quality of the current sakura group is in a good state.

Since temperature is positively correlated with humidity, and the temperature is higher, care may be taken to irrigate to maintain proper humidity.

Because the temperature is inversely related to the pH value of the soil, the change of the pH value of the soil under the high-temperature condition needs to be paid attention to, and the fertilization strategy is timely adjusted.

The comprehensive evaluation is that by considering the interrelationship between the ecological health indexes, a more comprehensive ecological environment quality evaluation is provided.

The introduction of normalization factors ensures that the composite score is not distorted by the excessive weight of a certain index.

The efficiency is improved, potential problems are identified in advance, and the resource utilization efficiency is improved by taking targeted measures.

Through the series of steps, the garden management department can not only obtain more accurate and comprehensive ecological environment quality assessment, but also make more effective management and maintenance measures according to the information, thereby better protecting and promoting the healthy growth of garden plants.

In the existing landscape plant ecological pressure prediction method, the problem that a plurality of methods only depend on historical data to predict, neglect current ecological state information and seasonal variation rules, and result in inaccurate prediction results is generally considered. Conventional time series models often do not adapt well to dynamic changes in the environment, especially under the influence of climate change and sudden events (e.g. extreme weather). A simple statistical model may not capture complex nonlinear relationships, affecting prediction accuracy.

In order to solve the problems, the application provides a method for predicting ecological pressure trend faced by garden plants in future period by combining a preliminary ecological health portrait, a seasonal ecological factor database and a time sequence model of historical ecological data and adopting a machine learning algorithm. By the method, the future ecological pressure can be predicted more comprehensively and accurately, so that more scientific management advice is provided.

Optionally, the method for predicting ecological pressure trend of the forest plants in future period by adopting a machine learning algorithm comprises extracting current ecological state information from the preliminary ecological health representation by combining the current ecological state information in the preliminary ecological health representation, the seasonal ecological factor database and the time sequence model of the historical ecological dataAnd obtain seasonal factors from the seasonal ecological factor databaseUse of historical ecological dataBuilding a time series model based on a trained machine learning modelCombining the current ecological state informationSeasonal factorAnd time series of historical ecological dataPredicting future ecological pressure;

Calculating predicted ecological pressure using the following formula:

;

Wherein, Is the adjustment coefficient of the light source,Is each time point in the historical ecological dataIs used for the degree of contribution of (a),Is a constant value, which is set to be a constant value,Is the average value of the historical ecological data,Is an error term.

Preliminary ecological health portrait) The comprehensive description is formed after the current plant and the environmental state thereof are comprehensively evaluated, and the overall health condition and the surrounding environmental condition of the plant are reflected.

Seasonal ecological factor) Factors which have important influence on plant growth in different seasons, such as spring germination temperature, summer high-temperature drought, autumn defoliation humidity, winter low-temperature freeze injury and the like.

Historical ecological data) By all records of information collected over time about the ecology of a particular area, including but not limited to weather conditions, soil characteristics, vegetation growth, etc.

Time series model-a statistical method for analyzing the relationship between chronologically arranged data points to help discover potential trends and patterns.

Machine learning model [ ]) A class of computational models that can "learn" from the data and make predictions or decisions based thereon.

Regulating coefficient [ ]) Parameters for adjusting the degree of influence of historical data on the prediction result.

Contribution degree [ ]) Representing the contribution degree of each time point t in the historical ecological data to the prediction result.

Constant [ (constant ]) A fixed value for adjusting the influence of the average of the historical data.

Error term [ (ll ]) Representing the deviation between the model predictions and the actual values.

In the embodiment of the application, it is assumed that a city park needs to predict the ecological pressure that a cherry blossom tree group may face in the next year. The following steps are specific:

and extracting the information such as the health state, the environment suitability (such as illumination and humidity), the incidence rate of diseases and insect pests and the like of the current cherry tree from the preliminary ecological health portrait.

Assume current ecological state information= [0.8,0.6,0.2], Which respectively represent good health status (0.8), moderate environment suitability (0.6) and low incidence rate of plant diseases and insect pests (0.2).

And acquiring key ecological factors of the current season from the seasonal ecological factor database.

Assuming the current spring, seasonal factors= [15 ℃,70% ], Representing germination temperature (15 ℃) and relative humidity (70%), respectively.

Ecological data including temperature, humidity, rainfall, etc. was collected monthly over the past five years.

And constructing a time sequence model by using an LSTM model, and capturing the variation trend of the ecological pressure.

Trained machine learning modelIs a random forest model.

Historical ecological data

= [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] Representing the month average temperature over the last five years.

Adjustment coefficient=0.5, Constant=1.0。

Calculating an average value of historical data=20.5。

According to the formula:

;

Hypothesis machine learning model The output of (2) was 0.7, the contribution degree at each time pointRespectively is [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].

The calculation process comprises the following steps:

;

Supposing error terms Then finally predicting ecological pressure。

The prediction results show that the cherry blossom tree group may face higher ecological pressure in the next year, especially under high temperature and drought conditions.

Park management department can prepare in advance according to the method, and corresponding irrigation, sunshade and pest control measures are adopted to reduce the influence of ecological pressure on the cherry tree.

The embodiment of the application provides more comprehensive ecological pressure prediction by combining the current ecological state information, seasonal factors and historical data. The machine learning model can be better adapted to the dynamic change of the environment, and the prediction precision is improved. Based on more accurate prediction results, the management department can make more scientific and reasonable decisions.

Through the series of steps, the garden management department can not only obtain more accurate and comprehensive ecological pressure prediction, but also make more effective management and maintenance measures according to the information, thereby better protecting and promoting the healthy growth of garden plants.

Fig. 2 is a schematic structural diagram of an ecological assessment system for monitoring environment of garden plants according to an embodiment of the present application, as shown in fig. 2, the apparatus includes:

The receiving module 21 is used for receiving the multielement environmental parameters related to the plant health condition in the garden environment acquired by the sensor network in real time;

the evaluation module 22 is used for analyzing the multiple environmental parameters by using a preset ecological health index system, dynamically evaluating the ecological environment quality of the garden plants and forming a preliminary ecological health portrait;

The prediction module 23 is used for predicting the ecological pressure trend of the garden plants in the future period according to the preliminary ecological health portrait and combining the historical ecological data and the seasonal variation rules;

a formulation simulation module 24, configured to formulate and simulate a plurality of ecological intervention measures according to the ecological pressure trend, and evaluate an ecological environment improvement effect under each of the ecological intervention measures;

And the feedback module 25 is used for determining the optimal ecological intervention measures according to the evaluation result and feeding back the optimal ecological intervention measures to the garden management department to guide ecological maintenance actions.

An ecological assessment system for monitoring the environment of a landscape plant described in fig. 2 may implement an ecological assessment method for monitoring the environment of a landscape plant described in the embodiment shown in fig. 1, and its implementation principle and technical effects will not be described again. The specific manner in which the various modules and units perform the operations in the ecological assessment system for monitoring the environment of garden plants in the above embodiments has been described in detail in the embodiments related to the method, and will not be described in detail here.

In one possible design, an ecological assessment system for landscape plant environment monitoring of the embodiment of fig. 2 may be implemented as a computing device, as shown in fig. 3, which may include a storage component 31 and a processing component 32;

the storage component 31 stores one or more computer instructions for execution by the processing component 32.

The processing component 32 is used for receiving multiple environmental parameters related to plant health conditions in a garden environment collected in real time by a sensor network, analyzing the multiple environmental parameters by utilizing a preset ecological health index system, dynamically evaluating the ecological environment quality of the garden plants to form a preliminary ecological health portrait, predicting the ecological pressure trend of the garden plants in a future period according to the preliminary ecological health portrait and combining historical ecological data and seasonal variation rules, formulating and simulating a plurality of ecological intervention measures, evaluating the ecological environment improvement effect under each ecological intervention measure, determining the optimal ecological intervention measure according to the evaluation result, and feeding back the optimal ecological intervention measure to a garden management department to guide ecological maintenance actions.

Wherein the processing component 32 may include one or more processors to execute computer instructions to perform all or part of the steps of the methods described above. Of course, the processing component may also be implemented as 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 elements for executing the methods described above.

The storage component 31 is configured to store various types of data to support operations at the terminal. The memory component may be implemented by any type or combination of volatile or nonvolatile memory devices 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 or optical disk.

Of course, the computing device may necessarily include other components as well, such as input/output interfaces, display components, communication components, and the like.

The input/output interface provides an interface between the processing component and a peripheral interface module, which may be an output device, an input device, etc.

The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, and the like.

The computing device may be a physical device or an elastic computing host provided by the cloud computing platform, and at this time, the computing device may be a cloud server, and the processing component, the storage component, and the like may be a base server resource rented or purchased from the cloud computing platform.

The embodiment of the application also provides a computer storage medium which stores a computer program, and the computer program can realize the ecological assessment method for monitoring the environment of the garden plants in the embodiment shown in the figure 1 when being executed by a computer.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.

It should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same, and although the present application has been described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the technical solution described in the above-mentioned embodiments may be modified or some technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solution of the embodiments of the present application.

Claims

1. An ecological assessment method for garden plant environment monitoring, comprising:

Determining optimal ecological intervention measures according to the evaluation results, and feeding back the optimal ecological intervention measures to a garden management department to guide ecological maintenance actions;

the analyzing the multiple environmental parameters by using a preset ecological health index system, dynamically evaluating the ecological environment quality of garden plants and forming a preliminary ecological health portrait comprises the following steps:

Mapping the collected multiple environmental parameters to corresponding ecological health indexes based on a preset ecological health index system, wherein the ecological health index system covers key environmental factors required by plant growth;

carrying out standardization treatment on the mapped ecological health index;

according to the importance of the ecological health indexes and the interaction relation among the ecological health indexes, calculating a comprehensive score reflecting the ecological environment quality of the garden plants;

dividing different ecological environment quality grades according to the calculated comprehensive scores to construct a preliminary ecological health portrait of the garden plant, wherein the ecological health portrait visually displays the health state and potential risk points of the environment where the plant is located;

According to the importance of the ecological health index and the interaction relation between the ecological health indexes, the comprehensive score reflecting the ecological environment quality of the garden plants is calculated, and the method comprises the following steps:

Acquiring each ecological health index WhereinAnd each of the ecological health indicatorsWith corresponding importance weights;

;

Wherein, ,The number of observations is indicated and,AndRespectively the firstEcological health index in secondary observationAndIs used for the observation of the (a),AndRespectively are ecological health indexesAndIs calculated as follows:;

The predicting the ecological pressure trend of the garden plant in the future period according to the preliminary ecological health portrait and combining the historical ecological data and the seasonal variation law comprises the following steps:

Acquiring and arranging current ecological environment quality information reflected in the preliminary ecological health portrait, wherein the current ecological environment quality information comprises key indexes of plant health status, environment suitability and pest and disease occurrence rate;

Collecting and analyzing long-term historical ecological data related to the garden plants, wherein the historical ecological data at least comprise environmental parameter records, plant growth conditions and pest occurrence conditions in the past several growth cycles;

Identifying key ecological factors affecting the growth of garden plants by combining with seasonal variation rules, wherein the key ecological factors comprise germination temperature in spring, high-temperature drought in summer, leaf-falling humidity in autumn and low-temperature freeze injury in winter so as to establish a seasonal ecological factor database;

Modeling the historical ecological data by using a time sequence analysis method to capture a time mode and trend of ecological pressure of garden plants;

combining the current ecological state information in the preliminary ecological health portrait, the seasonal ecological factor database and the time sequence model of the historical ecological data, and predicting ecological pressure trend faced by the garden plants in a future period by adopting a machine learning algorithm;

Combining the current ecological state information in the preliminary ecological health portrait, the seasonal ecological factor database and the time sequence model of the historical ecological data, predicting ecological pressure trend faced by the garden plants in the future period by adopting a machine learning algorithm, and comprising the following steps:

extracting current ecological state information from the preliminary ecological health portrait And obtain seasonal factors from the seasonal ecological factor database;

Use of historical ecological dataConstructing a time sequence model;

based on trained machine learning model Combining the current ecological state informationSeasonal factorAnd time series of historical ecological dataPredicting future ecological pressure;

Calculating predicted ecological pressure using the following formula:

;

2. The method according to claim 1, wherein said formulating and simulating a plurality of ecological interventions according to said ecological pressure trend, and evaluating an ecological environment improvement effect under each of said ecological interventions, comprises:

Aiming at the predicted ecological pressure trend, combining the physiological characteristics and ecological requirements of garden plants, designing a plurality of ecological intervention measures;

simulating response changes of garden plants and surrounding environments thereof after implementing the ecological intervention measures;

And evaluating the ecological environment improvement effect under each ecological intervention measure based on the simulation result.

3. The method according to claim 2, wherein determining the optimal ecological intervention based on the evaluation result and feeding back the optimal ecological intervention to the garden management department to guide the ecological maintenance action comprises:

Comprehensively evaluating the simulation result by adopting a multi-objective optimization algorithm, and determining one or more ecological intervention measures with the optimal ecological environment improvement effect as an optimal scheme;

And feeding back the optimal ecological intervention measures to a garden management department in the form of electronic documents or reports so as to guide ecological maintenance actions.

4. An ecological assessment system for landscape plant environmental monitoring, comprising:

the feedback module is used for determining the optimal ecological intervention measures according to the evaluation results and feeding back the optimal ecological intervention measures to the garden management department to guide ecological maintenance actions;

carrying out standardization treatment on the mapped ecological health index;

;

Wherein, ,The number of observations is indicated and,AndRespectively the firstEcological health index in secondary observationAndIs used for the observation of the (a),AndRespectively are ecological health indexesAndIs calculated as follows: The method according to claim 1, wherein the predicting the ecological pressure trend of the garden plant in the future period according to the preliminary ecological health portrait and combining the historical ecological data and the seasonal variation law comprises:

Use of historical ecological dataConstructing a time sequence model;

Calculating predicted ecological pressure using the following formula:

;

5. A computing device, comprising a processing component and a storage component, wherein the storage component stores one or more computer instructions for execution by the processing component, the one or more computer instructions implementing an ecological assessment method for landscape plant environmental monitoring as claimed in any one of claims 1-3.

6. A computer storage medium, characterized in that a computer program is stored, which computer program, when being executed by a computer, implements an ecological assessment method for monitoring the environment of garden plants as claimed in any one of claims 1-3.