Forest tree planting article selection prediction method and system based on big data

Abstract

The invention discloses a big data-based forest tree planting selection prediction method and system, and relates to the technical field of forest tree planting selection, and the method comprises the following steps: determining a prediction target and a prediction time range, collecting genome data, phenotype data, planting data and growth environment data of a prediction target planting region, and integrating the data into a data set; preprocessing the data set, extracting features related to forest planting varieties in the data set, including uses and key characters, and screening out key features influencing the forest planting varieties through a statistical method; and establishing a prediction model according to the data characteristics and the prediction target. Through application of the big data technology, cross-regional and cross-variety forest tree planting data are integrated and analyzed, key factors influencing forest tree growth, yield and quality are identified, a comprehensive evaluation system is constructed based on the common factors, errors caused by a single standard are reduced, and the accuracy of a prediction result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of forest tree planting selection, and in particular to a forest tree planting selection prediction method and system based on big data. Background Art

[0002] Selection of tree varieties for forest planting refers to the process of selecting the most suitable tree varieties or species for planting in a region according to multiple factors such as specific environmental conditions, market demand, economic benefits and ecological goals during the process of forest planting. At present, with the global attention to environmental protection and sustainable development, forestry, as an indispensable part of the ecosystem, its sustainable development is particularly important. With the continuous development of information technology, forestry informatization and intelligence have also become a convenient way in the process of selection of tree varieties for forest planting. The selection method of tree varieties for forest planting based on big data has also emerged. It uses massive data and advanced analysis technology to optimize the process of selecting tree varieties for forest planting. For example, for the three trees of poplar, willow and catalpa, growth rate, stress resistance, wood quality and ecological adaptability are important quantitative traits for selection. Through big data analysis technology, the performance of different varieties in these aspects can be evaluated more scientifically. At the same time, in order to further improve the efficiency of forest planting, the use of big data for selection of tree varieties for forest planting will be more widely used and developed.

[0003] In the existing technology, since different tree species and trees for different purposes have different evaluation standards, there will be certain errors in the selection prediction results. Therefore, it is our problem to establish a unified evaluation mechanism to improve the evaluation accuracy and the accuracy of the prediction results. Therefore, a forest planting selection prediction method and system based on big data is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for predicting tree planting selection based on big data to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] In the first aspect, a method for predicting tree planting selection based on big data comprises the following steps:

[0007] Step 1: Determine the prediction target and prediction time range, collect genomic data, phenotypic data, planting data and growth environment data of the prediction target planting area, and integrate them into a data set; genomic data refers to the genome sequence or single nucleotide polymorphism data of different tree varieties in the target planting area, growth environment data refers to soil quality, climate temperature, humidity, rainfall, light, topography and other data, and planting data refers to information such as tree varieties, growth conditions, yield, and occurrence of pests and diseases;

[0008] Step 2: Preprocess the data set to improve data quality. Preprocessing includes data cleaning, data standardization, and processing missing values. Features related to tree species in the data set are extracted, including uses and key traits, such as growth rate, adaptability, economic value, and ecological benefits. Statistical methods are used to screen out key features that affect tree species. If there are too many features, dimensionality reduction techniques can be used to reduce the number of features and improve model training efficiency.

[0009] Step 3: Establish a prediction model based on data characteristics and prediction targets, and use the preprocessed data set to train the prediction model;

[0010] Step 4: Establish a comprehensive evaluation index system based on the purpose and key characteristics of trees to ensure the comprehensiveness and accuracy of the prediction results. Use the trained prediction model to predict the tree varieties in the target planting area and output the prediction results for each tree variety.

[0011] A further improvement of the technical solution of the present invention is that the integration process of the data set is:

[0012] Step 101, determine the predicted tree species, such as poplar, willow, catalpa, fast-growing forest, economic forest, ornamental forest, etc., and the specific prediction targets, such as growth rate, wood quality, disease and pest resistance, etc., and determine the time range of the prediction target; such as short-term, medium-term, long-term,

[0013] Step 102, obtaining genome sequences of different tree species through a database, using drones and sensors to automatically collect visible characteristics of different tree species, such as growth rate, wood quality, pest and disease resistance, etc., and collecting climate data of the target planting area, such as temperature, humidity, rainfall, light intensity, soil data, such as soil type, pH value, nutrient content, and topographic data, such as altitude, slope, terrain features, etc.;

[0014] Step 103, the genomic data, phenotypic data, planting data and growth environment data are integrated and associated in a unified format to form a complete data set, and the data set is analyzed to understand the distribution, correlation and potential problems of the data.

[0015] A further improvement of the technical solution of the present invention is that the step of screening the key features is:

[0016] Step 201, check and fill missing values ​​in the data set, identify and process outliers in the data set, and perform data standardization;

[0017] Step 202, extracting the genomic characteristics, phenotypic characteristics and environmental characteristics of the forest tree varieties, and calculating the correlation coefficient and characteristic evaluation coefficient between the prediction target and the genomic characteristics, phenotypic characteristics and environmental characteristics, and outputting the correlation analysis report and the characteristic evaluation report;

[0018] Step 203, extracting key features that have an impact on the prediction of forest tree varieties based on the correlation analysis report and the characteristic evaluation report;

[0019] Step 204: extracting the features that have the greatest impact on the prediction target based on the key features.

[0020] A further improvement of the technical solution of the present invention is that the calculation formula of the correlation coefficient is:

[0021]

[0022] in, represents the correlation coefficient, Represents the genome feature G i and phenotypic characteristics P j The Pearson correlation coefficient between and Represents the genome features G i and phenotypic characteristics P j Environmental characteristics k The Pearson correlation coefficient between Indicates environmental characteristics E k The correlation coefficient with itself is the variance of the feature.

[0023] A further improvement of the technical solution of the present invention is that the calculation formula of the characteristic evaluation coefficient is:

[0024]

[0025] Among them I F represents the characteristic evaluation coefficient, N represents the total number of features in the feature set, and w i,f Indicates that in the i-th tree in the random forest, λ i represents the singular value in partial least squares regression PLS, w fi and v fi represents the weight of feature f and the i-th component of the response weight in PLS, 1+λ i Represents the adjustment factor used to balance w fi and v fi The square term, log(1+F f ) indicates that the F statistic is logarithmically transformed.

[0026] A further improvement of the technical solution of the present invention is that the process of training the prediction model is:

[0027] Step 301, dividing the preprocessed data set into a training set, a validation set, and a test set, and using the training set data to train the prediction model;

[0028] Step 302, using the validation set to validate the prediction model, evaluate the performance of the model, and select key features for retraining based on the validation results and the characteristic evaluation report, and adjust the prediction model;

[0029] Step 303, use the test set to test the adjusted prediction model and output the test results; the test set should be kept independent from the training set and the validation set to ensure the objectivity and reliability of the evaluation results.

[0030] A further improvement of the technical solution of the present invention is that: the tree species to be planted in the predicted target planting area are:

[0031] Step 401, determine the purpose of the trees, and based on the purpose of the trees, identify the key traits that affect the performance of the trees, and obtain various evaluation values, such as biomass, trunk morphology, leaf area index, disease resistance, etc.

[0032] Step 402, using the trained prediction model to predict the tree species in the target planting area, and output the prediction results in the form of charts; such as bar charts, line charts, radar charts, etc., to intuitively display the scores or rankings of each tree species in terms of growth rate, wood quality, stress resistance, etc. The prediction results may include the scores or rankings of each species in terms of growth rate, wood quality, stress resistance, etc.

[0033] Step 403, associate each evaluation value with the prediction result to obtain a comprehensive score, analyze the comprehensive score of each variety, identify the best variety, and output a detailed report.

[0034] A further improvement of the technical solution of the present invention is that the output process of the comprehensive evaluation result is:

[0035] Step 501, based on the predicted ecological and environmental conditions of the target planting area, evaluate the adaptability of each tree variety in the target planting area, consider whether its growth habit, drought resistance, cold resistance, disease and pest resistance, etc. match the regional conditions, and analyze the market potential, competitiveness and market demand trend of each tree variety in the prediction results based on the market demand for different tree varieties;

[0036] Step 502, according to the comprehensive score, score each tree planting variety and calculate the comprehensive score of each tree planting variety to obtain a comprehensive score result;

[0037] Step 503, based on the comprehensive scoring results, select the tree species with the highest score as the tree planting species. Meanwhile, the following factors are considered for final decision making: ecological risk: assess the ecological risk that may be brought by the selected species, and take corresponding measures to prevent and mitigate it; technical feasibility: consider the availability and feasibility of planting technology to ensure that the selected species can be planted and managed smoothly; policy support: understand and consider the support and restrictions of relevant policies on planting species to obtain more policy preferences and support;

[0038] Step 504, output the comprehensive scoring results in the form of a chart to clarify the comprehensive score and ranking of each tree variety. At the same time, planting suggestions are put forward, including variety selection, planting technology, management measures, etc., to provide scientific basis and guidance for actual planting.

[0039] In a second aspect, a tree planting selection prediction system based on big data includes a data management center, wherein the data management center is communicatively connected to a data acquisition module, a data preprocessing module, a data analysis and mining module, a prediction model building module, an evaluation module, a user interaction and feedback module, and a privacy protection module, wherein each module is connected by electrical signals;

[0040] The data collection module is used to collect the planting data of forest tree species, and to organize, classify and store the collected planting data to ensure the accuracy and completeness of the data, provide a basis for subsequent data analysis, and adopt cloud storage or distributed database system to support large-scale data storage and efficient retrieval;

[0041] The data preprocessing module is used to clean, integrate and standardize the collected planting data to ensure the quality and consistency of the planting data, improve the availability and accuracy of the data, provide a reliable data source for subsequent analysis, and extract feature data from the preprocessed data. These features have predictive value for forest tree planting selection;

[0042] The data analysis and mining module is used to use big data technology of data mining and machine learning to analyze the collected planting data, and to mine the correlation, trend and law between the data, and output the data analysis and mining report to provide a basis for the prediction of forest tree planting selection;

[0043] The prediction model building module: based on the data analysis and mining report, builds a forest tree planting variety prediction model, and uses the feature data to train the prediction model, uses the trained prediction model to perform prediction analysis on new planting data, outputs prediction results, provides suggestions for forest tree planting selection, adjusts model parameters according to the evaluation results, and optimizes model performance;

[0044] The evaluation module is used to evaluate the effect of the prediction model and adjust the model parameters through feedback from actual planting results;

[0045] The user interaction and feedback module is used to provide a friendly user interface, so that growers can obtain the prediction results of product selection, collect user feedback on the prediction results, optimize and improve the prediction model, including online query, report generation, early warning prompts, user guides, etc.;

[0046] The privacy protection module is used to protect the security of planting data and prevent data leakage and unauthorized access.

[0047] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0048] 1. The present invention provides a method and system for predicting tree planting selection based on big data. By collecting and analyzing the genome, phenotype and environmental big data of tree varieties, the adaptability and potential performance of each variety in different ecological environments are comprehensively evaluated. According to environmental conditions and variety characteristics, the planting structure is optimized, and the varieties most suitable for the local environment are selected for planting. By rationally allocating planting resources, growers can reduce unnecessary investment, improve resource utilization efficiency, reduce production costs, significantly reduce planting risks, and increase planting success rates.

[0049] 2. The present invention provides a method and system for predicting tree planting selection based on big data. By using big data technology, it integrates and analyzes cross-regional and cross-variety tree planting data, identifies key factors affecting tree growth, yield and quality, and constructs a comprehensive evaluation system based on these common factors to reduce errors caused by a single standard and improve the accuracy of prediction results.

[0050] 3. The present invention provides a method and system for predicting tree planting selection based on big data. By collecting and analyzing the genome, phenotype and environmental big data of tree varieties, the adaptability and potential performance of each variety in different ecological environments are comprehensively evaluated. According to environmental conditions and variety characteristics, the planting structure is optimized, and the varieties most suitable for the local environment are selected for planting. By rationally allocating planting resources, growers can reduce unnecessary investment, improve resource utilization efficiency, reduce production costs, significantly reduce planting risks, and increase planting success rates.

[0051] 4. The present invention provides a method and system for predicting tree planting selection based on big data, predicting the growth performance of different varieties of trees under different conditions, providing personalized selection suggestions, and optimizing planting structure. At the same time, by accurately predicting the growth cycle and yield of trees, the production plan can be arranged more reasonably, resource waste can be reduced, and economic benefits can be improved. In addition, the system can also provide early warning of pests and diseases, helping growers take timely measures to reduce losses and ensure planting benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0053] Figure 1 is a flow chart of the method of the present invention;

[0054] Figure 2 Screening flow chart for key features of the present invention;

[0055] Figure 3 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Embodiment 1, as Figure 1 , 2As shown, the present invention provides a method for predicting tree planting selection based on big data, comprising the following steps: Step 1, determining the prediction target and the prediction time range, collecting genome data, phenotypic data, planting data and growth environment data of the predicted target planting area, and integrating them into a data set; the genome data is the genome sequence or single nucleotide polymorphism data of different tree planting varieties in the target planting area, the growth environment data is soil quality, climate temperature, humidity, rainfall, light, etc., as well as topography and other data, and the planting data is information such as the variety, growth condition, yield, and occurrence of pests and diseases of the tree planting; the integration process of the data set is: determining the predicted tree planting variety, such as poplar, willow, catalpa, fast-growing forest, economic forest, ornamental forest, etc., and the specific prediction target, such as growth rate, wood quality, resistance to pests and diseases, etc., and determining the time range of the prediction target; such as short-term, medium-term, long-term, Obtain the genome sequences of different tree species through the database, use drones and sensors to automatically collect visible characteristics of different tree species, such as growth rate, wood quality, and pest and disease resistance, and collect climate data of the target planting area, such as temperature, humidity, rainfall, and light intensity, soil data, such as soil type, pH value, and nutrient content, and topographic data, such as altitude, slope, and terrain characteristics. Integrate and associate genomic data, phenotypic data, planting data, and growth environment data in a unified format to form a complete data set, and analyze the data set to understand the distribution, correlation, and potential problems of the data. Calculate the mean, standard deviation, maximum value, minimum value, and other statistical quantities of each data indicator to understand the distribution of the data, use the correlation coefficient to analyze the correlation between different data indicators, and identify the key factors affecting the growth rate, wood quality, and pest and disease resistance of trees;

[0058] Step 2: preprocess the data set to improve data quality. Preprocessing includes data cleaning, data standardization, processing missing values, etc., extracting features related to tree species in the data set, including uses and key traits, such as growth rate, adaptability, economic value, ecological benefits, etc., and screening out key features that affect tree species through statistical methods. If the number of features is too large, the number of features can be reduced through dimensionality reduction technology to improve model training efficiency. The steps for screening key features are: check and fill missing values ​​in the data set, identify and process outliers in the data set, and perform data standardization; extract genomic features, phenotypic features, and environmental features of tree species, and calculate the correlation coefficient and characteristic evaluation coefficient between the predicted target and the genomic features, phenotypic features, and environmental features, and output the correlation analysis report and characteristic evaluation report at the same time; extract key features that affect the prediction of tree species based on the correlation analysis report and characteristic evaluation report; extract the features that have the greatest impact on the predicted target based on the key features; the calculation formula for the correlation coefficient is:

[0059]

[0060] in, represents the correlation coefficient, Represents the genome feature G i and phenotypic characteristics P j The Pearson correlation coefficient between and Represents the genome features G i and phenotypic characteristics P j Environmental characteristics k The Pearson correlation coefficient between Indicates environmental characteristics E k The correlation coefficient with itself is the variance of the feature;

[0061] The calculation formula of the characteristic evaluation coefficient is:

[0062]

[0063] Among them I F represents the characteristic evaluation coefficient, N represents the total number of features in the feature set, and w i,f Indicates that in the i-th tree in the random forest, λ i represents the singular value in partial least squares regression PLS, w fi and v fi represents the weight of feature f and the i-th component of the response weight in PLS, 1+λ i Represents the adjustment factor used to balance w fi and v fi The square term, log(1+F f ) indicates logarithmic transformation of F statistic;

[0064] Step 3: Establish a prediction model based on data characteristics and prediction targets, and use the preprocessed data set to train the prediction model. The process of training the prediction model is as follows: divide the preprocessed data set into training set, validation set and test set, and use the training set data to train the prediction model. Use the validation set to verify the prediction model, evaluate the performance of the model, and select key features for retraining based on the verification results and characteristic evaluation report, and adjust the prediction model. Use the test set to test the adjusted prediction model and output the test results. The test set should be independent of the training set and validation set to ensure the objectivity and reliability of the evaluation results.

[0065] Step 4: Establish a comprehensive evaluation index system based on the purpose and key traits of trees to ensure the comprehensiveness and accuracy of the prediction results. Use the trained prediction model to predict the tree varieties in the predicted target planting area and output the prediction results of each tree variety. Predicting the tree varieties in the target planting area is as follows: determine the purpose of the trees, and identify the key traits that affect the performance of the trees based on the purpose of the trees, and obtain various evaluation values; such as biomass, trunk morphology, leaf area index, disease resistance, etc. Use the trained prediction model to predict the tree varieties in the target planting area and output the prediction results in the form of charts; such as bar charts, line charts, radar charts, etc., to intuitively display the scores or rankings of each tree variety in terms of growth rate, wood quality, stress resistance, etc. The prediction results may include the scores or rankings of each variety in terms of growth rate, wood quality, stress resistance, etc., associate each evaluation value with the prediction result, obtain a comprehensive score, analyze the comprehensive score of each variety, identify the best variety, and output a detailed report.

[0066] Step 5, comprehensively evaluate the forecast results in combination with ecological environment requirements and market demand, output the comprehensive evaluation results, and select the most suitable forest tree varieties for planting based on the comprehensive evaluation results; the output process of the comprehensive evaluation results is as follows: according to the ecological environment conditions of the predicted target planting area, evaluate the adaptability of each forest tree variety in the target planting area, consider whether its growth habits, drought resistance, cold resistance, disease and pest resistance, etc. match the regional conditions, and analyze the market potential, competitiveness and market demand trend of each forest tree variety in the forecast results based on the market demand for different forest tree varieties; score each forest tree variety based on the comprehensive score and calculate the comprehensive score of each forest tree variety to obtain the comprehensive score. Comprehensive scoring results; based on the comprehensive scoring results, select the tree species with the highest score as the tree planting variety. At the same time, consider the following factors to make the final decision: ecological risk: evaluate the ecological risks that may be brought about by the selected varieties, and take corresponding measures to prevent and mitigate them; technical feasibility: consider the availability and feasibility of planting technology to ensure that the selected varieties can be smoothly planted and managed; policy support: understand and consider the support and restrictions of relevant policies on planting varieties in order to obtain more policy preferences and support; output the comprehensive scoring results in the form of charts to clarify the comprehensive score and ranking of each tree variety. At the same time, put forward planting suggestions, including variety selection, planting technology, management measures, etc., to provide scientific basis and guidance for actual planting.

[0067] Embodiment 2, as Figure 3As shown, on the basis of Example 1, the present invention further provides a forest tree planting selection prediction system based on big data, including a data management center, the data management center is communicatively connected with a data acquisition module, a data preprocessing module, a data analysis and mining module, a prediction model building module, an evaluation module, a user interaction and feedback module, and a privacy protection module, wherein the modules are connected by electrical signals;

[0068] Data collection module: used to collect planting data of tree species, including climate conditions, soil types, water source conditions, historical planting records, market demand, tree growth cycle, occurrence of pests and diseases, etc., and organize, classify and store the collected planting data to ensure the accuracy and completeness of the data, provide a basis for subsequent data analysis, and use cloud storage or distributed database systems to support large-scale data storage and efficient retrieval;

[0069] Data preprocessing module: used to clean, integrate and standardize the collected planting data to ensure the quality and consistency of the planting data, improve the availability and accuracy of the data, provide a reliable data source for subsequent analysis, and extract feature data from the preprocessed data. These features have predictive value for forest tree planting selection;

[0070] Data analysis and mining module: It is used to apply big data technology of data mining and machine learning to analyze the collected planting data, and to mine the correlation, trend and law between the data, and output data analysis and mining reports to provide a basis for the prediction of forest tree planting selection;

[0071] Prediction model building module: Based on data analysis and mining reports, a forest tree planting variety prediction model is built, and the prediction model is trained using feature data. The trained prediction model is used to perform prediction analysis on new planting data, output prediction results, provide suggestions for forest tree planting selection, adjust model parameters according to evaluation results, and optimize model performance;

[0072] Evaluation module: used to evaluate the effect of the prediction model and adjust the model parameters through feedback from actual planting results;

[0073] User interaction and feedback module: used to provide a friendly user interface to enable growers to obtain product selection prediction results, collect user feedback on the prediction results, optimize and improve the prediction model, including online query, report generation, early warning prompts, user guides, etc.;

[0074] Privacy protection module: used to protect the security of planting data and prevent data leakage and unauthorized access.

[0075] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for predicting tree planting selection based on big data, characterized by: The following steps are involved: Step 1: determine the prediction target and prediction time range, collect genomic data, phenotypic data, planting data and growth environment data of the prediction target planting area, and integrate them into a data set; Step 2: preprocess the data set, extract the features related to the tree species in the data set, including usage and key traits, and screen out the key features that affect the tree species through statistical methods; Step 3: Establish a prediction model based on data characteristics and prediction targets, and use the preprocessed data set to train the prediction model; Step 4: Establish a comprehensive evaluation index system based on the purpose and key characteristics of the trees, use the trained prediction model to predict the tree varieties in the predicted target planting area, and output the prediction results of each tree variety; Step 5: Combine ecological and environmental requirements and market demands to conduct a comprehensive evaluation of the forecast results, output the comprehensive evaluation results, and select the most suitable forest species for planting based on the comprehensive evaluation results.

2. The method for predicting tree planting selection based on big data according to claim 1 is characterized by: The integration process of the dataset is as follows: Step 101, determining the predicted tree species and the specific prediction target, and determining the time range of the prediction target; Step 102, obtaining genome sequences of different tree species through a database, using drones and sensors to automatically collect visible features of different tree species, and collecting climate data, soil data, and topographic data of the target planting area; Step 103, the genomic data, phenotypic data, planting data and growth environment data are integrated and associated in a unified format to form a complete data set, and the data set is analyzed to understand the distribution, correlation and potential problems of the data.

3. The method for predicting tree planting selection based on big data according to claim 2 is characterized by: The screening steps of the key features are: Step 201, check and fill missing values ​​in the data set, identify and process outliers in the data set, and perform data standardization; Step 202, extracting the genomic characteristics, phenotypic characteristics and environmental characteristics of the forest tree varieties, and calculating the correlation coefficient and characteristic evaluation coefficient between the prediction target and the genomic characteristics, phenotypic characteristics and environmental characteristics, and outputting the correlation analysis report and the characteristic evaluation report; Step 203, extracting key features that have an impact on the prediction of forest tree varieties based on the correlation analysis report and the characteristic evaluation report; Step 204: extracting the features that have the greatest impact on the prediction target based on the key features.

4. The method for predicting tree planting selection based on big data according to claim 3 is characterized by: The calculation formula of the correlation coefficient is: in, represents the correlation coefficient, Represents the genome feature G i and phenotypic characteristics P j The Pearson correlation coefficient between and Represents the genome features G i and phenotypic characteristics P j Environmental characteristics k The Pearson correlation coefficient between Indicates environmental characteristics E k The correlation coefficient with itself is the variance of the feature.

5. The method for predicting tree planting selection based on big data according to claim 4 is characterized by: The calculation formula of the characteristic evaluation coefficient is: Among them I F represents the characteristic evaluation coefficient, N represents the total number of features in the feature set, and w i,f Indicates that in the i-th tree in the random forest, λ i represents the singular value in partial least squares regression PLS, w fi and v fi represents the weight of feature f and the i-th component of the response weight in PLS, 1+λ i Represents the adjustment factor used to balance w fi and v fi The square term, log(1+F f ) indicates that the F statistic is logarithmically transformed.

6. The method for predicting tree planting selection based on big data according to claim 3 is characterized by: The process of training the prediction model is as follows: Step 301, dividing the preprocessed data set into a training set, a validation set, and a test set, and using the training set data to train the prediction model; Step 302, using the validation set to validate the prediction model, evaluate the performance of the model, and select key features for retraining based on the validation results and the characteristic evaluation report, and adjust the prediction model; Step 303: Use the test set to test the adjusted prediction model and output the test result.

7. The method for predicting tree planting selection based on big data according to claim 6 is characterized by: The tree species planted in the predicted target planting area are: Step 401, determine the purpose of the trees, and based on the purpose of the trees, identify the key traits that affect the performance of the trees, and obtain various evaluation values; Step 402, using the trained prediction model to predict the tree species in the target planting area, and outputting the prediction results in the form of graphs; Step 403, associate each evaluation value with the prediction result to obtain a comprehensive score, analyze the comprehensive score of each variety, identify the best variety, and output a detailed report.

8. The method for predicting tree planting selection based on big data according to claim 7 is characterized by: The output process of the comprehensive evaluation results is as follows: Step 501, based on the predicted ecological environment conditions of the target planting area, evaluate the adaptability of each tree variety in the target planting area, and analyze the market potential, competitiveness and market demand trend of each tree variety in the predicted results based on the market demand for different tree varieties; Step 502, according to the comprehensive score, score each tree planting variety and calculate the comprehensive score of each tree planting variety to obtain a comprehensive score result; Step 503, according to the comprehensive scoring result, selecting the tree species with the highest score as the tree planting species; Step 504, output the comprehensive scoring results in the form of a chart to clarify the comprehensive score and ranking of each tree species.

9. A system for predicting tree planting selection based on big data, used to implement a method for predicting tree planting selection based on big data as claimed in any one of claims 1 to 8, characterized in that: It includes a data management center, which is communicatively connected to a data acquisition module, a data preprocessing module, a data analysis and mining module, a prediction model building module, an evaluation module, a user interaction and feedback module, and a privacy protection module, wherein the modules are connected by electrical signals; The data collection module is used to collect the planting data of forest tree species, and to organize, classify and store the collected planting data; The data preprocessing module is used to clean, integrate and standardize the collected planting data, and extract feature data from the preprocessed data; The data analysis and mining module is used to use the big data technology of data mining and machine learning to analyze the collected planting data, and to mine the correlation, trend and law between the data, and output the data analysis and mining report; The prediction model building module: based on the data analysis and mining report, builds a forest tree planting variety prediction model, and uses the feature data to train the prediction model, uses the trained prediction model to perform prediction analysis on new planting data, outputs the prediction results, adjusts the model parameters according to the evaluation results, and optimizes the model performance; The evaluation module is used to evaluate the effect of the prediction model and adjust the model parameters through feedback from actual planting results; The user interaction and feedback module is used to provide a friendly user interface, so that growers can obtain the prediction results of product selection, collect user feedback on the prediction results, and optimize and improve the prediction model; The privacy protection module is used to protect the security of planting data and prevent data leakage and unauthorized access.

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