Rapid positioning and early warning system for ambrosia trifida in forest planting

Abstract

The invention discloses a rapid positioning and early warning system for ambrosia trifida in forest planting, and the system comprises a climate and growth prediction module which analyzes historical climate data and the growth law of ambrosia trifida, combines with a climate model, and predicts the rapid positioning and early warning of ambrosia trifida under different climate change conditions. The growth mode, the expansion trend and the possible distribution area of ambrosia trifida are determined; the real-time monitoring and data acquisition module is used for collecting environmental data and the growth state of ambrosia trifida in real time; the data fusion and analysis module is used for comprehensively processing the multi-source data collected by the climate and growth prediction module and the real-time monitoring module, evaluating the growth trend, spatial distribution and ecological risk of ambrosia trifida and providing accurate data support for subsequent early warning and decision making; and the early warning and decision support module automatically generates early warning information of different risk levels according to the growth trend and the risk assessment result generated by the data fusion and analysis module. The method has the advantages of high positioning precision, strong adaptive adjustment capability, accurate grading early warning mechanism and the like.

Description

Technical Field

[0001] The invention relates to the technical field of early warning identification, and in particular to a rapid positioning early warning system for Ambrosia trilobata in forest planting. Background Art

[0002] In the context of global climate change, the growth of plant species has been affected unprecedentedly, especially the expansion speed and distribution range of alien invasive species have gradually increased, threatening the stability of the ecological environment and the maintenance of biodiversity. As a serious alien invasive plant, the growth characteristics and diffusion patterns of Ambrosia trifida make it a major hidden danger in forest and agricultural ecosystems. The growth performance of Ambrosia trifida under different climatic conditions, soil types and environmental factors is extremely different, which makes it extremely difficult to accurately identify and issue timely warnings in a dynamic environment. Especially in the context of increasingly severe climate change and complex and changeable environment, it is facing huge technical challenges to accurately monitor its growth status and distribution trends, and then make effective intervention decisions.

[0003] First, the unpredictability of climate change has brought great uncertainty to the prediction of plant growth patterns. The growth of three-lobed ragweed is affected by a variety of climate factors such as temperature, precipitation, humidity, and light. In traditional climate models, changes in climate conditions are often inferred based on historical data. However, the intensification of climate change has made these predictions more complex and difficult to be precise. For example, rising temperatures may make three-lobed ragweed grow more vigorously in some areas, but at the same time, it may also limit its growth due to insufficient water resources. The complexity of this interaction requires that the model must consider the interaction of multiple variables. This multivariate cross-influence, especially on different time scales (such as the superposition of seasonal changes and long-term climate trends), makes it difficult for a single prediction model to be accurate.

[0004] Secondly, the spatial heterogeneity of plant growth is also a key factor that increases the difficulty of prediction. Three-lobed ragweed is not only affected by climatic factors, but also closely related to soil type, topography and the competitive state of surrounding vegetation. The spatial differences in these environmental factors lead to uneven growth distribution of three-lobed ragweed. Even in different microenvironments in the same area, the growth of three-lobed ragweed may be completely different. For example, in humid areas, its growth rate and density may be higher, while in arid areas it may be inhibited. This spatial heterogeneity makes it difficult to fully understand its growth status through static monitoring methods (such as regular sampling, manual inspection, etc.). In this case, the use of technical means such as remote sensing, drone imaging and high-precision sensors for real-time and dynamic monitoring becomes a necessary means, but these technologies themselves also face multiple challenges such as data collection, processing and analysis. Especially in complex ecosystems, how to process data from different sources and in different formats and integrate them with prediction models to provide accurate early warning information is a major technical problem.

[0005] In addition, dynamically changing environmental conditions place higher demands on the system's adaptive capabilities. With the continuous changes in climate and the evolution of the ecological environment, the growth pattern of three-lobed ragweed may also change accordingly. For example, in years with frequent droughts or extreme weather, three-lobed ragweed may show different growth patterns and may even become resistant to certain management measures. Therefore, the system must not only have a static growth trend prediction function, but also have strong adaptive capabilities to adjust monitoring and early warning strategies in real time and respond to environmental changes in a timely manner. To achieve this, the system must integrate real-time data collection, analysis, and feedback mechanisms, which places higher demands on the design of the technical architecture and data processing capabilities. Especially when facing big data and massive information, how to ensure the efficiency and accuracy of data processing has become another major technical challenge.

[0006] In addition, the invasion of ragweed is usually not limited to a certain area, but has a wide spatial expansion, which requires the early warning system to monitor simultaneously in a large-scale area and make different response strategies according to the specific conditions of different regions. However, this regional difference is not only reflected in the growth rate and density distribution, but also in the ecological characteristics and adaptability of management measures in different regions. This requires the system to be able to make flexible adjustments in large-scale monitoring and management, and to provide personalized decision support in real time according to the risk level, ecological impact and management needs of different regions.

[0007] To sum up, the reasons why it is technically difficult to accurately identify and issue early warnings under different times and environmental conditions are mainly reflected in: the complex impact of climate change on plant growth, the data processing challenges brought about by environmental spatial heterogeneity, the requirements for system adaptability, and the complex management needs of regional differences. How to achieve accurate prediction, monitoring and risk assessment of three-leaved ragweed through efficient technical means under changing environmental conditions has become a technical problem that needs to be urgently solved in the current field of ecological protection. Summary of the invention

[0008] In order to solve the problems in the prior art, the present invention provides a rapid positioning and early warning system for Ambrosia trifida in forest planting.

[0009] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0010] A rapid positioning and early warning system for Ambrosia trilobata in forest planting, comprising:

[0011] The climate and growth prediction module analyzes historical climate data and the growth patterns of three-leaf ragweed and combines climate models to predict the growth pattern, expansion trend and possible distribution area of ​​three-leaf ragweed under different climate change conditions.

[0012] Real-time monitoring and data collection module, which collects environmental data and the growth status of three-leaved ragweed in real time;

[0013] The data fusion and analysis module comprehensively processes the multi-source data collected by the climate and growth prediction module and the real-time monitoring module, and evaluates the growth trend, spatial distribution and ecological risk of Ambrosia trilobata through deep learning, machine learning and spatiotemporal analysis methods, and provides accurate data support for subsequent early warning and decision-making;

[0014] The early warning and decision support module automatically generates early warning information of different risk levels based on the growth trends and risk assessment results generated by the data fusion and analysis module.

[0015] Furthermore, the climate and growth prediction module includes:

[0016] The climate pattern recognition algorithm was used to classify historical climate data, extract the relevant factors of the growth of three-leaved ragweed under specific climate conditions, introduce an adaptive climate adjustment algorithm, and combine the trend of climate change to predict the growth pattern under different climate conditions through regression analysis;

[0017] Based on the Logistic model, the growth curve of three-leaved ragweed was modeled in combination with climatic conditions. The formula is as follows:

[0018]

[0019] Among them, P(t) represents the number of three-leaved ragweed at a certain time t, K is the environmental carrying capacity, r is the growth rate, and t0) is the time when growth begins;

[0020] Correction formula for temperature, humidity and precipitation on growth rate:

[0021]

[0022] Where T(t) is the current temperature, T max is the maximum temperature that affects plant growth, H(t) is the current humidity, and H opt is the optimal humidity range, r0 is the basic growth rate;

[0023] Use the long short-term memory network to make time series predictions for future climate. Combined with the climate change trend, the climate conditions in the next few months to years are predicted, and the growth of three-leaved ragweed is adjusted accordingly. For example, the LSTM model is used to predict future climate conditions. The formula is:

[0024] y t =σ(W h h t-1 +W x x t +b)

[0025] Among them, y t is the predicted climate data, h t-1 is the hidden state of the previous time step, x t is the input data of the current time step, σ is the activation function, W h , W x , b are weight matrices and bias terms.

[0026] Furthermore, the real-time monitoring and data acquisition module includes:

[0027] Environmental data perception submodule:

[0028] Sensor network: Sensors are deployed in the forest to monitor temperature, humidity, light, soil moisture, and pH value in real time. Based on the early warning information provided by the climate and growth prediction module, this module will deploy sensors in potential high-risk areas in advance to ensure that data in important areas can be obtained in a timely manner;

[0029] Temperature and humidity sensor: records the temperature and humidity changes in the air in real time, helping to identify whether the plant's growth environment meets the growth requirements of three-leaf ragweed;

[0030] Soil sensors: including soil moisture, temperature, and pH sensors, which monitor the moisture content and pH of the soil so that the monitoring strategy can be adjusted in time when soil conditions change;

[0031] Meteorological sensors: monitor microclimate changes, such as wind speed, precipitation, and air pressure, and predict the potential impact of climate change trends on ragweed;

[0032] Regularly scan plant images in the forest and analyze the distribution density and plant height characteristics of three-leaved ragweed based on image recognition technology;

[0033] The CNN algorithm is used to process images in real time to identify the location, density and growth status of Ambrosia trilobata.

[0034] Through multispectral cameras and infrared thermal imaging equipment, information in different bands can be obtained to identify plant health status, drought level and other growth performance;

[0035] Intelligent scheduling and real-time data synchronization submodule:

[0036] Automatically adjust monitoring strategies based on the output of the climate and growth prediction module;

[0037] Dispatching sensors and monitoring equipment based on real-time weather forecasts and known high-risk areas;

[0038] Real-time data upload and processing submodule:

[0039] All collected data are preliminarily processed near the sensor and uploaded to the cloud via wireless network.

[0040] Furthermore, the data fusion and analysis module includes:

[0041] Data preprocessing and standardization submodule:

[0042] Unified preprocessing of the time series forecast data from the climate and growth forecast module and the environment and growth monitoring data from the real-time monitoring module;

[0043] Spatiotemporal data fusion submodule:

[0044] The spatiotemporal data fusion algorithm is used to integrate climate forecast and real-time monitoring data into a unified spatiotemporal distribution prediction model through the mapping of spatiotemporal coordinates. Combined with the spatiotemporal convolutional neural network, the network can extract spatiotemporal features from multi-source data and process the complex relationship between time series changes and spatial distribution, so that the fused data can be consistent in time and space.

[0045] Data analysis and trend forecasting submodule:

[0046] Using multivariate regression analysis and Bayesian inference models, we analyzed the growth trend, distribution characteristics, and possible future expansion areas of Ambrosia trilobata based on the fused data, and provided a growth trend forecast for a period of time in the future.

[0047] Risk assessment and distribution range prediction submodule:

[0048] Based on the integrated climate, environment, soil and plant growth data, geographic information system and machine learning models are used in combination with spatial autoregression models to predict growth areas and risks in order to predict the potential expansion areas and possible risk hot spots of trifoliate ragweed in the future.

[0049] Furthermore, the early warning and decision support module includes:

[0050] Warning generation and risk assessment submodule:

[0051] Generate risk warning information of different levels based on the output of the data fusion and analysis module;

[0052] After generating risk warnings, activate corresponding response mechanisms according to different risk levels;

[0053] The warning generation and risk assessment submodule will combine historical data and forecast models based on the current warning information;

[0054] Real-time feedback and adaptive adjustment submodule:

[0055] Adaptive adjustments are made through a real-time data feedback mechanism, automatically updating risk assessments and intervention recommendations when new monitoring data or changes in climate conditions are detected.

[0056] Compared with the prior art, the present invention has the following technical advances:

[0057] The present invention forms a comprehensive monitoring network by integrating data from the climate and growth prediction module, the real-time monitoring and data acquisition module, and the data fusion and analysis module. The system can perform multi-level analysis from multiple dimensions such as climate change, soil conditions, plant growth dynamics, and meteorological data to predict the growth trend and distribution range of three-leaved ragweed. This multi-dimensional data fusion greatly improves the accuracy of the prediction and has higher accuracy and adaptability than traditional single monitoring methods (such as relying on manual monitoring or single meteorological data).

[0058] Through the real-time monitoring and data acquisition module, the system can achieve 24 / 7 uninterrupted dynamic monitoring and obtain environmental data related to the growth of three-lobed ragweed in real time. These data are quickly transmitted and processed to ensure that the system can adjust the early warning and intervention strategies according to environmental changes at any time. The design of the real-time feedback mechanism can ensure that the system automatically updates the prediction model according to the newly acquired data, so that the system has the ability to adaptively adjust, and can respond to changes in climate, environment and other conditions in a short time to avoid delaying the implementation of management measures. Through the early warning and decision support module, the present invention can classify the growth risk of three-lobed ragweed and provide specific intervention suggestions under different risk levels. Different from the traditional "one-size-fits-all" method, the system can dynamically generate early warning information of different levels such as low risk, medium risk and high risk according to real-time data and prediction information, and provide managers with personalized intervention measures. This precise graded early warning can help managers adopt appropriate management plans according to actual conditions, thereby avoiding waste of resources and minimizing the ecological threat of invasive species.

[0059] Due to the strong spatial heterogeneity and temporal fluctuation of the growth of three-lobed ragweed, traditional monitoring methods are often difficult to be comprehensive and efficient. However, this system uses remote sensing technology, drone imaging and other means to monitor and evaluate the growth status of plants in real time on a wide range of spatial scales. The system combines geographic information systems with multi-source sensor data, so that the system can still maintain a high degree of accuracy in large-scale woodlands and different environmental conditions, achieving dual guarantees of spatial and temporal accuracy. Through this high-precision spatial data collection and analysis, the system can accurately locate the specific distribution of three-lobed ragweed, discover potential invasion hotspots in a timely manner, and avoid omissions.

[0060] Overall, the advantages of the present invention lie in its high-precision, multi-dimensional data processing capabilities, real-time feedback and adaptive adjustment functions, accurate risk assessment and graded warning mechanism, and scientific decision-making support, which enable it to effectively respond to the challenges of the invasive alien species, Ambrosia trifoliata. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0062] In the attached picture:

[0063] Figure 1 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0064] The following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0065] like Figure 1 As shown, the present invention discloses a rapid positioning and early warning system for Ambrosia trilobata in forest planting, including: a climate and growth prediction module, a real-time monitoring and data acquisition module, a data fusion and analysis module, and an early warning and decision support module.

[0066] Specifically, the climate and growth forecast module includes:

[0067] 1. Plant growth prediction model based on climate change dynamics

[0068] The core design of this module is a multi-level climate and plant growth prediction framework. This embodiment combines the traditional climate prediction model with plant growth dynamic feedback to improve the accuracy and practicality of the prediction through the following steps:

[0069] Integration of historical climate data: First, the system collects climate data (including temperature, humidity, precipitation, etc.) from the past 30 years or longer, and uses a climate classification model to classify the climate data to identify the climate types and periodic patterns that affect the growth of three-leaf ragweed.

[0070] Growth pattern modeling: The growth pattern of three-leaf ragweed was mathematically modeled through a nonlinear dynamic model, taking into account the influence of climatic factors such as temperature, humidity, precipitation, etc. on its different growth stages. This model not only predicts the growth stage of three-leaf ragweed through climatic factors (temperature, humidity, light intensity, etc.), but also introduces a biofeedback mechanism, that is, under specific climatic conditions, the growth pattern of the plant can be adjusted according to environmental changes.

[0071] Adjustment mechanism based on climate change: In actual predictions, the system will take into account the long-term trends of climate change (such as global warming, intensified seasonal changes, etc.), dynamically predict the future climate through a time series prediction algorithm, and embed the prediction results into the plant growth model to automatically adjust the growth prediction of three-leaved ragweed according to climate fluctuations.

[0072] The implementation process is as follows:

[0073] Climate data preprocessing and modeling:

[0074] The historical climate data were classified using climate pattern recognition algorithms (such as Kmeans clustering), and the relevant factors of the growth of three-leaved ragweed under specific climate conditions were extracted. An adaptive climate adjustment algorithm was introduced, and the growth patterns under different climate conditions were predicted through regression analysis in combination with the trend of climate change.

[0075] Growth dynamic feedback mechanism:

[0076] Based on the Logistic model, the growth curve of three-leaved ragweed was modeled in combination with climatic conditions. The formula is as follows:

[0077]

[0078] Among them, P(t) represents the number of three-leaved ragweed at a certain time t, K is the environmental carrying capacity, r is the growth rate, and t0) is the time when growth begins.

[0079] Correction formula for temperature, humidity and precipitation on growth rate:

[0080]

[0081] Where T(t) is the current temperature, T max is the maximum temperature that affects plant growth, H(t) is the current humidity, and H opt is the optimal humidity range, r0 is the basic growth rate.

[0082] Time Series Climate Prediction Models:

[0083] Use the long short-term memory network to make time series predictions for future climate. Combined with the climate change trend, the climate conditions in the next few months to years are predicted, and the growth of three-leaved ragweed is adjusted accordingly. For example, the LSTM model is used to predict future climate conditions. The formula is:

[0084] y t =σ(W h h t-1 +W x x t +b)

[0085] Among them, y t is the predicted climate data, h t-1 is the hidden state of the previous time step, x t is the input data of the current time step, σ is the activation function, W h , W x , b are weight matrices and bias terms.

[0086] The execution flow of the module is:

[0087] 1. Climate data collection and classification: The system first collects historical climate data from public climate data sources (such as NOAA, WorldClim, etc.) and classifies them.

[0088] 2. Preliminary setting of the growth model: Based on the growth characteristics of R. trifoliate ragweed, a logistic growth model was established to determine the initial growth rate and environmental carrying capacity.

[0089] 3. Climate trend analysis and adjustment: Use the LSTM model to predict future climate and dynamically adjust the growth model based on climate change.

[0090] 4. Feedback mechanism and update: Based on the continuous input of real-time climate data and sensor data (such as soil moisture, temperature, etc.), the system will automatically adjust the parameters of the plant growth model (such as growth rate, carrying capacity, etc.) to achieve adaptive optimization.

[0091] 5. Forecast output: Ultimately, the system will output a growth trend map of ragweed in the next few months to years, marking high-risk areas and potential outbreak periods.

[0092] This module combines the impact of real-time climate change on plant growth, can dynamically adjust the prediction parameters, make the prediction results more forward-looking and accurate, and use time series prediction to dynamically predict future climate, so as to predict the growth performance of three-leaf ragweed under different climatic conditions, identify potential explosive growth in advance, combine multiple factors such as climatic conditions and soil moisture, and ensure that the prediction of three-leaf ragweed growth is more in line with actual ecological changes through adaptive adjustment of the growth model. By integrating climate prediction technology with biological growth models and combining real-time climate and environmental data, not only can the growth trend of three-leaf ragweed be accurately predicted, but also the prediction model can be dynamically adjusted in the context of climate change to ensure the high adaptability and accuracy of the system. The innovation of this system is not only reflected in the adaptive mechanism of the climate model, but also in its deep understanding of plant growth and real-time adjustment capabilities in complex environments, providing strong decision-making support for forestry management.

[0093] Specific real-time monitoring and data collection modules include:

[0094] The core goal of the real-time monitoring and data acquisition module is to dynamically collect and transmit environmental and growth data through precise sensor networks and intelligent imaging technology, so as to seamlessly connect with the results of the climate and growth prediction module, thereby achieving efficient tracking and real-time adjustment of the growth of three-leaved ragweed. This module should not only monitor the climate, soil, and the growth status of the plant itself in a timely manner, but also deploy sensors and monitoring equipment in advance according to the future warning information provided by the climate and growth prediction module, optimize resource allocation and monitoring strategies, and build a comprehensive perception network through diversified data collection methods, so that warnings and adjustments can respond to environmental changes immediately.

[0095] The system process includes the following sub-modules:

[0096] 1. Environmental data perception submodule:

[0097] Sensor network: Various sensors are deployed in the forest to monitor environmental parameters such as temperature, humidity, light, soil moisture, pH value, etc. in real time. This module will deploy sensors in potential high-risk areas in advance based on the early warning information provided by the climate and growth prediction module to ensure that data in important areas can be obtained in a timely manner.

[0098] Temperature and humidity sensor: Real-time record of temperature and humidity changes in the air, helping to identify whether the plant's growth environment meets the growth requirements of three-leaf ragweed.

[0099] Soil sensors: including soil moisture, temperature, and pH sensors, which monitor the moisture content and pH of the soil so that the monitoring strategy can be adjusted in time when soil conditions change.

[0100] Meteorological sensors: monitor microclimate changes such as wind speed, precipitation, air pressure, etc., and predict the potential impact of climate change trends on ragweed.

[0101] 2. Image acquisition and recognition submodule:

[0102] Drone and remote sensing image technology: Use drones or fixed cameras for regular scanning, use high-resolution camera equipment to capture images of plants in the woodland, and use image recognition technology to analyze the distribution density, plant height and other characteristics of three-leaf ragweed.

[0103] Deep learning and convolutional neural network (CNN): The CNN algorithm is used to process images in real time to identify the location, density and growth status of three-leaved ragweed.

[0104] Multispectral and thermal imaging technology: Multispectral cameras and infrared thermal imaging equipment are used to obtain information in different bands and identify plant health status, drought level and other growth performance, which is particularly important in monitoring areas with sparse or dense vegetation.

[0105] 3. Intelligent scheduling and real-time data synchronization submodule:

[0106] Adaptive monitoring scheduling system: Automatically adjusts monitoring strategies based on the output of the climate and growth prediction module. For example, when extreme temperature or precipitation changes are predicted in the future, the system will automatically adjust the sampling frequency of sensors or deploy new sensors.

[0107] Resource scheduling algorithm: Based on real-time weather forecasts and known high-risk areas, sensors and monitoring equipment are intelligently scheduled. For example, when the climate model predicts high temperatures or drought, the system will prioritize moisture sensors and temperature and humidity sensors to ensure that sufficient environmental data is collected in key areas.

[0108] 4. Real-time data upload and processing submodule:

[0109] Edge computing nodes: All collected data will be initially processed near the sensor and uploaded to the cloud via wireless networks. Edge computing nodes will be used to process and filter data, reducing the burden on the central server and ensuring real-time response.

[0110] Low-power wide area network (LPWAN): Adopt low-power network protocols such as LoRaWAN and NB-IoT to ensure low-power and efficient data transmission over a large area.

[0111] The Climate and Growth Forecast module provides the module with forecasts of future climate conditions and the growth dynamics of Ambrosia trifoliata. This information will guide real-time monitoring and data collection strategies at multiple levels to ensure monitoring accuracy and optimal allocation of resources. Specifically:

[0112] High-risk area warning and sensor deployment:

[0113] Based on the high-risk areas output by the climate and growth prediction module (such as abnormal drought or excessively high temperatures that may occur in a certain area), the system will deploy additional soil moisture sensors, temperature and humidity sensors, and meteorological sensors in these areas in advance to ensure that key environmental data changes are captured in real time when climate change occurs.

[0114] For example, if it is predicted that the temperature in the area will rise significantly in the next few weeks, the system will proactively increase the sampling frequency of the temperature and humidity sensors to ensure a more sensitive response to climate change.

[0115] Adjust data collection frequency and accuracy:

[0116] If the climate and growth prediction module analyzes that the growth momentum of three-leaved ragweed is strong in a certain period of time (for example, it is predicted that there will be a favorable humid climate), the system will adjust the data collection frequency based on this information and increase attention to the growth conditions of the plants, such as strengthening the monitoring of growth rate and density changes.

[0117] For example, if climate conditions are predicted to favor the rapid spread of ragweed, the sensor system will increase the frequency of image acquisition in specific areas (especially woodland edges and open spaces) to enable rapid response and timely warning.

[0118] Assuming that the sampling frequency f depends on the output of the climate prediction model (such as precipitation change ΔP) and soil moisture change ΔH, the adjustment formula is:

[0119] f new =f base ×(1+α·ΔP+β·ΔH)

[0120] Among them, fbase is the basic sampling frequency, α and β are the weight coefficients of the climate and growth prediction model, ΔP is the predicted precipitation change, and ΔH is the soil moisture change.

[0121] The deep learning-based image recognition model can output plant density estimation. Assuming that the plant area ratio extracted by image recognition is A area , the plant growth density D can be estimated by the following formula:

[0122]

[0123] Among them, A total is the total area of ​​the monitoring area, A area is the area covered by three-leaved ragweed, and the final output D is the growth density in percentage form.

[0124] Real-time growth status adjustment:

[0125] The real-time monitoring data will be combined with the results of the climate and growth prediction module to generate a dynamically updated growth model. For example, if the climate and growth prediction module indicates an increase in precipitation in the future and soil moisture begins to rise, the system can infer that the growth of three-leaved ragweed may accelerate, and thus adjust the key areas of image acquisition and monitoring frequency to ensure that changes in the growth process are captured.

[0126] The advantages of this module are: the real-time monitoring and data acquisition module can efficiently and dynamically adjust the monitoring strategy based on the results of the climate and growth prediction module through precise sensor layout, image recognition technology and intelligent scheduling system, so as to achieve accurate tracking of the growth of three-leaf ragweed. In the context of climate change and changes in plant growth status, real-time data acquisition not only ensures the comprehensiveness and timeliness of monitoring, but also provides accurate basic data support for subsequent predictions and early warnings, thereby effectively realizing the ecological management of forest land and accurate early warning of three-leaf ragweed.

[0127] Specifically, the data fusion and analysis module includes:

[0128] The data fusion and analysis module is a key link in the entire system. It carries the task of organically combining the data from the climate and growth prediction module with the real-time monitoring module. This module uses data fusion technology, combined with machine learning and statistical modeling methods, to analyze data from different sources, extract the growth trend, distribution range and potential risks of three-leaved ragweed, and conduct further accurate early warning and resource allocation based on this. Through accurate trend prediction, spatial distribution analysis and risk assessment, this module provides a strong basis for decision support.

[0129] The working framework of the data fusion and analysis module includes the following sub-modules:

[0130] 1. Data preprocessing and standardization submodule:

[0131] First, the time series forecast data from the climate and growth forecast module and the environmental and growth monitoring data from the real-time monitoring module are uniformly preprocessed. This process involves standardizing and normalizing data of different formats, different precisions, and different sources to ensure the effectiveness of subsequent analysis.

[0132] 2. Spatiotemporal data fusion submodule:

[0133] Since the data of the climate and growth prediction module are time-series, and the data of the real-time monitoring module are spatially distributed, data fusion needs to cross the time and space dimensions. This embodiment will adopt a spatiotemporal data fusion algorithm (for example, based on a spatiotemporal convolutional neural network or a combination of a long short-term memory network and an image segmentation model) to integrate climate forecasts and real-time monitoring data into a unified spatiotemporal distribution prediction model through the mapping of spatiotemporal coordinates.

[0134] Combined with the spatiotemporal convolutional neural network, the network can extract spatiotemporal features from multi-source data and process the complex relationship between temporal changes and spatial distribution, so that the fused data can be consistent in time and space.

[0135] For example, assuming that the growth density D(x,t) of three-leaved ragweed in a certain area changes with time t and spatial coordinate x, it can be modeled by the following formula:

[0136]

[0137] Among them, w ij Extract weights for spatiotemporal features, f(x i ,t j ) is the spatiotemporal feature mapping function, and D(x,t) is the plant growth density at the spatiotemporal point (x,t).

[0138] 3. Data analysis and trend prediction submodule:

[0139] Multivariate regression analysis and Bayesian inference models were used to analyze the growth trends, distribution characteristics and possible future expansion areas of three-leaved ragweed based on the fused data, especially in areas where plant growth is greatly affected by climate change. The model provided growth trend forecasts for a period of time in the future by modeling the relationship between environmental factors and growth behavior.

[0140] Trend prediction model: For example, a multivariate regression model (e.g., regression analysis based on climate factors and soil data) is used to predict the growth rate r(t) of ragweed. The formula is:

[0141] r(t)=β0+β1T(t)+β2H(t)+β3P(t)+ε

[0142] Wherein, T(t) is the temperature at time t, H(t) is the humidity, P(t) is the precipitation, β0, β1, β2, β3 are regression coefficients, and ε is the error term. This formula helps this embodiment to predict the growth rate of three-leaved ragweed under the influence of climate change in a certain period of time in the future.

[0143] 4. Risk assessment and distribution range prediction submodule:

[0144] Based on the integrated climate, environment, soil and plant growth data, this embodiment uses a geographic information system and a machine learning model, combined with a spatial autoregressive model to predict growth areas and risks. Through these models, the system can predict the potential expansion areas and possible risk hotspots of three-leaved ragweed in the future based on the growth trends and climatic conditions of the plants.

[0145] The spatial autoregression model is used to deal with the correlation and local autocorrelation in spatial data. The formula is as follows:

[0146] y=ρW y +Xβ+ε

[0147] Among them, y is the response variable of spatial data (such as plant growth density), W is the adjacency matrix, X is the external explanatory variable (such as climate, soil parameters, etc.), ρ is the spatial lag coefficient, β is the regression coefficient, and ε is the error term.

[0148] The innovations of this module are:

[0149] Combining spatiotemporal convolutional neural networks with long short-term memory networks, we can process nonlinearities and complex associations in spatiotemporal data, and achieve deep integration of multi-source data such as climate forecasting, environmental monitoring, and plant growth. Using machine learning algorithms and GIS technology, we can accurately predict the expansion range and potential risk areas of Ambrosia trilobata, and provide spatial visualization support for decision-making. Based on regression analysis and spatial autoregressive models, we can make accurate growth predictions to ensure the efficiency and scientificity of risk assessment and trend analysis.

[0150] Specifically, the early warning and decision support modules include:

[0151] The role of the early warning and decision support module is to convert the results of the climate and growth prediction module, the real-time monitoring and data collection module, and the data fusion and analysis module into specific early warning information, and provide operational suggestions based on this information. This module is not only responsible for the automatic generation and risk classification of early warnings, but also provides forestry managers with operational and scientific management measures through the early warning generation and risk assessment submodules. In order to improve the accuracy of decision-making and emergency response capabilities, the system adopts an adaptive decision-making model, combined with a real-time data feedback mechanism, to ensure that managers can quickly and accurately take intervention measures at different risk levels.

[0152] 1. Early warning generation and risk assessment submodule:

[0153] Based on the output of the data fusion and analysis module (e.g., growth trends, spatial distribution, environmental changes, etc.), the system generates risk warning information of different levels through early warning models (such as threshold judgment models based on multidimensional data and hierarchical decision trees). The risk assessment not only considers the growth dynamics of ragweed, but also integrates factors such as climate change, soil conditions, and existing plant coverage to generate a specific risk level.

[0154] Assume that the risk index R is derived from multiple variables (climate, soil moisture, plant density, etc.), where the weight of each variable is derived from historical data and model training:

[0155] R=αT+βH+γD+βP+ε

[0156] Among them, T is temperature, H is humidity, D is the density of three-leaved ragweed, P is precipitation, α, β, γ, δ are the weights of each variable, ε is the error term, and the risk index R will be mapped to different risk levels, such as low risk, medium risk, and high risk.

[0157] 2. Early warning response submodule:

[0158] After generating a risk warning, the system will activate the corresponding response mechanism according to different risk levels. For example, for high risk levels, the system may recommend immediate and strong ecological intervention measures (such as mechanical weeding, chemical control, etc.); while for low risk levels, it may only recommend regular monitoring and data updates. All response mechanisms will be adaptively adjusted based on real-time data and dynamic changes.

[0159] Response level classification:

[0160] For example, the warning information may include the following levels:

[0161] Low risk: Growth of ragweed is slow or limited, regular monitoring is recommended;

[0162] Medium risk: The growth of ragweed has accelerated, and it is recommended to strengthen monitoring and control when necessary;

[0163] High risk: Three-leaved ragweed grows rapidly and may threaten the forest ecology. It is recommended to take immediate measures, such as mechanical removal and chemical weeding.

[0164] 3. Decision support and intervention submodule:

[0165] The early warning generation and risk assessment submodule will provide managers with specific operational suggestions based on the current early warning information, combined with historical data and prediction models. These suggestions are not only based on the current growth status, but also take into account factors such as climate change and soil conditions to ensure the rationality and pertinence of intervention measures. Managers can choose appropriate management plans through the decision-making framework provided by the system, such as choosing appropriate weeding methods, adjusting irrigation systems, optimizing forest planning, etc.

[0166] Decision support model: By simulating the effects of different intervention measures on the growth of three-leaf ragweed, the system can provide managers with intervention effect predictions. Assuming that the effect of intervention measure C on three-leaf ragweed is ΔD(C), the intervention effect prediction model is:

[0167] ΔD(C)=θ0+θ1·R+θ2·S(C)+ε

[0168] Among them, θ0 is the baseline impact, R is the risk level, S(C) is the effect function of intervention measure C, θ1, θ2 are coefficients, and ε is the error term. This model helps managers choose the best solution by predicting the effects of different measures.

[0169] 4. Real-time feedback and adaptive adjustment submodule:

[0170] In order to ensure the accuracy and timeliness of early warning and decision support, the system will make adaptive adjustments through a real-time data feedback mechanism. When the system detects new monitoring data or changes in climate conditions, it will automatically update risk assessments and intervention recommendations. In this way, early warning information and operational recommendations can be updated and optimized in a timely manner during the actual management process to avoid outdated information affecting decision-making.

[0171] Adaptive adjustment mechanism: Assume that the real-time feedback data is D feedback , the system will adjust the current decision-making strategy according to the new data, and the update formula is as follows:

[0172] R new =R old +β·D feedback

[0173] Among them, R new is the updated risk index, R oldis the previous risk index, and β is the feedback correction coefficient. This mechanism can ensure that managers receive the latest warning information and suggestions.

[0174] The future climate change and three-leaf ragweed growth trend data provided by the climate and growth prediction module will serve as the basis for risk assessment and early warning generation. By predicting future climate conditions, the early warning and decision support module can identify potential high-risk areas in advance, issue early warnings, and provide managers with early intervention suggestions. The real-time environmental data and the growth status of three-leaf ragweed provided by the real-time monitoring and data acquisition module will be combined with the output of the data fusion and analysis module to generate more accurate risk assessment and intervention suggestions. The feedback of real-time data also provides a basis for the adaptive adjustment of the system, so that management measures can be updated in time with environmental changes. The growth trend, spatial distribution, environmental changes and other information provided by the data fusion and analysis module are the basis for generating early warning information and decision support. The module combines these data to generate corresponding response mechanisms according to different risk levels and gives specific intervention suggestions.

[0175] The innovation of this module lies in:

[0176] Combining multiple dimensions such as climate change, soil conditions, and plant density, a weighted sum model ( is used to conduct risk assessment and automatically generate early warnings of different risk levels. The real-time data feedback mechanism is combined with the simulation of intervention effect prediction to ensure that managers can flexibly adjust management strategies and intervention measures based on the latest environmental changes and the growth status of three-leaf ragweed. An interactive decision support interface based on GIS and data visualization is provided to help managers intuitively view risk information in different areas and improve decision-making efficiency. By simulating the impact of different intervention measures on the growth of three-leaf ragweed and combining risk prediction, targeted operational suggestions are put forward, thereby improving the scientificity and accuracy of forestry management.

[0177] The early warning and decision support module transforms the results from climate and growth forecasts, real-time monitoring and data collection, and data fusion and analysis into timely and effective early warning information through precise risk assessment, hierarchical response mechanism, and adaptive early warning generation and risk assessment submodules, and provides scientific and reasonable intervention suggestions for forestry managers. Through this module, managers can take the most appropriate measures at different risk levels to effectively prevent and control the spread of three-leaved ragweed and ensure the ecological safety of forest land.

[0178] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of protection of the claims of the present invention.

Claims

1. A rapid positioning and early warning system for Ambrosia trilobata in forest planting, characterized in that: include: The climate and growth prediction module analyzes historical climate data and the growth patterns of three-leaf ragweed and combines climate models to predict the growth pattern, expansion trend and possible distribution area of ​​three-leaf ragweed under different climate change conditions. Real-time monitoring and data collection module, which collects environmental data and the growth status of three-leaved ragweed in real time; The data fusion and analysis module comprehensively processes the multi-source data collected by the climate and growth prediction module and the real-time monitoring module, and evaluates the growth trend, spatial distribution and ecological risk of Ambrosia trilobata through deep learning, machine learning and spatiotemporal analysis methods, and provides accurate data support for subsequent early warning and decision-making; The early warning and decision support module automatically generates early warning information of different risk levels based on the growth trends and risk assessment results generated by the data fusion and analysis module.

2. The rapid positioning and early warning system for Ambrosia trifida in forest planting according to claim 1, characterized in that: The climate and growth prediction module includes: The climate pattern recognition algorithm was used to classify historical climate data, extract the relevant factors of the growth of three-leaved ragweed under specific climate conditions, introduce an adaptive climate adjustment algorithm, and predict the growth pattern under different climate conditions through regression analysis in combination with the trend of climate change. Based on the Logistic model, the growth curve of three-leaved ragweed was modeled in combination with climatic conditions. The formula is as follows: Among them, P(t) represents the number of three-leaved ragweed at a certain time t, K is the environmental carrying capacity, r is the growth rate, and t0) is the time when growth begins; Correction formula for temperature, humidity and precipitation on growth rate: Where T(t) is the current temperature, T max is the maximum temperature that affects plant growth, H(t) is the current humidity, and H opt is the optimal humidity range, r0 is the basic growth rate; Use the long short-term memory network to make time series predictions for future climate. Combined with the climate change trend, the climate conditions in the next few months to years are predicted, and the growth of three-leaved ragweed is adjusted accordingly. For example, the LSTM model is used to predict future climate conditions. The formula is: y t =σ(W h h t-1 +W x x t +b) Among them, y t is the predicted climate data, h t-1 is the hidden state of the previous time step, x t is the input data of the current time step, σ is the activation function, W h , W x , b are weight matrices and bias terms.

3. The rapid positioning and early warning system for Ambrosia trifida in forest planting according to claim 2, characterized in that: The real-time monitoring and data acquisition module includes: Environmental data perception submodule: Sensor network: Sensors are deployed in the forest to monitor temperature, humidity, light, soil moisture, and pH value in real time. Based on the early warning information provided by the climate and growth prediction module, this module will deploy sensors in potential high-risk areas in advance to ensure that data in important areas can be obtained in a timely manner; Temperature and humidity sensor: records the temperature and humidity changes in the air in real time, helping to identify whether the plant's growth environment meets the growth requirements of three-leaf ragweed; Soil sensors: including soil moisture, temperature, and pH sensors, which monitor the moisture content and pH of the soil so that the monitoring strategy can be adjusted in time when soil conditions change; Meteorological sensors: monitor microclimate changes, such as wind speed, precipitation, and air pressure, and predict the potential impact of climate change trends on ragweed; Regularly scan plant images in the forest and analyze the distribution density and plant height characteristics of three-leaved ragweed based on image recognition technology; The CNN algorithm is used to process images in real time to identify the location, density and growth status of Ambrosia trilobata. Through multispectral cameras and infrared thermal imaging equipment, information in different bands can be obtained to identify plant health status, drought level and other growth performance; Intelligent scheduling and real-time data synchronization submodule: Automatically adjust monitoring strategies based on the output of the climate and growth prediction module; Dispatching sensors and monitoring equipment based on real-time weather forecasts and known high-risk areas; Real-time data upload and processing submodule: All collected data are preliminarily processed near the sensor and uploaded to the cloud via wireless network.

4. The rapid positioning and early warning system for Ambrosia trifida in forest planting according to claim 3, characterized in that: The data fusion and analysis module includes: Data preprocessing and standardization submodule: Unified preprocessing of the time series forecast data from the climate and growth forecast module and the environment and growth monitoring data from the real-time monitoring module; Spatiotemporal data fusion submodule: The spatiotemporal data fusion algorithm is used to integrate climate forecast and real-time monitoring data into a unified spatiotemporal distribution prediction model through the mapping of spatiotemporal coordinates. Combined with the spatiotemporal convolutional neural network, the network can extract spatiotemporal features from multi-source data and process the complex relationship between time series changes and spatial distribution, so that the fused data can be consistent in time and space. Data analysis and trend forecasting submodule: Using multivariate regression analysis and Bayesian inference models, we analyzed the growth trend, distribution characteristics, and possible future expansion areas of Ambrosia trilobata based on the fused data, and provided a growth trend forecast for a period of time in the future. Risk assessment and distribution range prediction submodule: Based on the integrated climate, environment, soil and plant growth data, geographic information system and machine learning models are used in combination with spatial autoregression models to predict growth areas and risks in order to predict the potential expansion areas and possible risk hot spots of trifoliate ragweed in the future.

5. The rapid positioning and early warning system for Ambrosia trifida in forest planting according to claim 4, characterized in that: The early warning and decision support module includes: Warning generation and risk assessment submodule: Generate risk warning information of different levels based on the output of the data fusion and analysis module; After generating risk warnings, activate corresponding response mechanisms according to different risk levels; The warning generation and risk assessment submodule will combine historical data and forecast models based on the current warning information; Real-time feedback and adaptive adjustment submodule: Adaptive adjustments are made through a real-time data feedback mechanism, automatically updating risk assessments and intervention recommendations when new monitoring data or changes in climate conditions are detected.

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