A road intelligent vertical greening method and system

Abstract

The present invention relates to a method and system for intelligent vertical greening of roads, comprising: obtaining historical growth data and historical environmental data of vegetation to be greened in a road during historical planting; constructing and solving a first objective function based on the historical growth data and the historical environmental data to obtain model parameters of a growth prediction model; predicting predicted growth data of the vegetation to be greened based on the growth prediction model; constructing and solving a second objective function based on the condition that the predicted growth data is satisfied to obtain a first variable for control measures during the greening process, and a second variable for auxiliary decision-making; constructing and solving a third objective function based on the condition that the predicted growth data is satisfied to obtain energy consumption and energy power demand during the greening process; generating a greening strategy for the vegetation to be greened based on the first variable, the second variable, the energy consumption, and the energy power demand, and performing vertical greening processing on the road based on the greening strategy. The present invention can improve the greening effect of road vegetation.

Description

Technical Field

[0001] The present invention relates to the field of irrigation water analysis technology, and in particular to a method, system, electronic device and non-transient computer-readable storage medium for intelligent vertical greening of roads. Background Art

[0002] Currently, intelligent vertical greening approaches for roads primarily utilize vertical greening systems, which achieve greening by planting plants on building facades or within urban spaces. By utilizing vertical space, this approach not only beautifies the urban environment and improves air quality, but also contributes to energy conservation and cooling. Common approaches include vertical gardens, green walls, and rooftop gardens, and have been widely adopted in some cities and buildings.

[0003] However, existing vertical greening systems face difficulties in long-term maintenance and management, particularly for high-rise buildings or large-scale greening projects, which require significant human and material resources. Furthermore, they have limitations in plant growth management, making them difficult to adapt to complex and changing urban environments and climatic conditions, resulting in unstable greening results. Furthermore, some systems still waste water resources and irrigation management, requiring further optimization and improvement. Summary of the Invention

[0004] In response to the technical problems existing in the prior art, the present invention provides a road intelligent vertical greening method, system, electronic device and non-transitory computer-readable storage medium that can improve the accuracy and convenience of road vegetation greening.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides a method for intelligent vertical greening of roads, the method comprising:

[0007] Obtain historical growth data and historical environmental data of vegetation to be greened on roads during historical planting;

[0008] constructing and solving a first objective function based on the historical growth data and the historical environmental data to obtain model parameters of a growth prediction model;

[0009] Predicting predicted growth data of the vegetation to be greened according to the growth prediction model;

[0010] Based on the condition that the predicted growth data is satisfied, a second objective function is constructed and solved to obtain a first variable of the control measure in the greening process and a second variable for auxiliary decision-making;

[0011] Based on the condition that the predicted growth data is satisfied, a third objective function is constructed and solved to obtain energy consumption and energy power requirements during the greening process;

[0012] Based on the first variable, the second variable, the energy consumption and the energy power demand, a greening strategy for the vegetation to be greened is generated, and vertical greening processing is performed on the road based on the greening strategy.

[0013] Optionally, constructing and solving the first objective function includes:

[0014] obtaining an initial growth prediction model;

[0015] Training the initial growth prediction model based on the training samples composed of the historical growth data and the historical environmental data, constructing the first objective function, and optimizing model parameters of the initial growth prediction model; the model parameters include a first weight, a bias term, and a slack variable;

[0016] Determine the optimized first weight, the bias term, and the slack variable to solve the first objective function.

[0017] Optionally, the first objective function is expressed as:

[0018] ;

[0019] Among them, J is the first objective function, is the first weight, is the slack variable, Q is the regularization matrix, C is the penalty parameter, λ is the weight of the time factor, and b is the slack variable. and the bias term obtained by the penalty parameter C.

[0020] Optionally, constructing and solving the second objective function includes:

[0021] Acquiring a plurality of initial first variables for predicting control measures in the greening process and a plurality of initial second variables for assisting decision-making;

[0022] Obtaining a first weight of each of the initial first variables and a second weight of each of the initial second variables;

[0023] Obtaining a third weight between each of the initial first variables and each of the initial second variables;

[0024] Based on the first weight, the second weight, and the third weight, performing weighted summation on a plurality of the first variables and a plurality of the second variables to obtain a first sum value;

[0025] The second objective function is constructed and solved based on the first sum value.

[0026] Optionally, the second objective function is expressed as:

[0027] ;

[0028] Among them, f is the second objective function, x is the first variable, y is the second variable, is the first weight, v is the second weight, is the initial first variable of the i-th control measure, represents the initial second variable of the j-th auxiliary decision, n is the total number of initial first variables, and m is the total number of initial second variables.

[0029] Optionally, constructing and solving the third objective function includes:

[0030] Obtaining a plurality of initial energy consumptions, a plurality of initial energy unit area consumptions, and a plurality of initial energy power requirements corresponding to a plurality of energy sources predicted during the greening process;

[0031] obtaining a fourth weight of each of the initial energy consumption amounts and a fifth weight of each of the initial energy power requirements;

[0032] The third objective function is constructed and solved based on the fourth weight, the second weight, the third weight, the multiple initial energy consumption amounts, the multiple initial energy unit area consumptions and the multiple initial energy power requirements.

[0033] Optionally, the third objective function is expressed as:

[0034]

[0035] in, is the third objective function, represents the jth initial energy consumption, represents the jth initial energy consumption per unit area, represents the jth initial energy power requirement, It is the fourth weight of total energy consumption. The fifth weight representing the energy power requirement, j is a serial number, and the total of the initial energy consumption is equal to the total of the initial energy power requirement, both of which are q.

[0036] Optionally, generating a greening strategy for the vegetation to be greened based on the first variable, the second variable, the energy consumption, and the energy power demand includes:

[0037] Greening the vegetation to be greened within a target period using the first variable, the second variable, the energy consumption, and the energy power demand to obtain a phased greening result;

[0038] Based on the phased greening results, the first variable, the second variable, the energy consumption and the energy power demand are adjusted to obtain the greening strategy.

[0039] Optionally, the acquisition of historical growth data and historical environmental data of the vegetation to be greened on the road during historical planting includes:

[0040] Acquiring initial data of the historical growth data and the historical environmental data;

[0041] Preprocessing the initial data to obtain preprocessed data;

[0042] Performing feature extraction on the preprocessed data to obtain feature data;

[0043] The characteristic data is subjected to data standardization processing to obtain the historical growth data and the historical environment data.

[0044] The present invention also provides a road intelligent vertical greening system, the system comprising:

[0045] A data acquisition module is used to obtain historical growth data and historical environmental data of the vegetation to be greened on the road during historical planting;

[0046] a parameter determination module, configured to construct and solve a first objective function based on the historical growth data and the historical environmental data to obtain model parameters of a growth prediction model;

[0047] A growth prediction module, configured to predict the predicted growth data of the vegetation to be greened according to the growth prediction model;

[0048] A variable determination module is used to construct and solve a second objective function based on the condition that the predicted growth data is satisfied, to obtain a first variable of the control measure in the greening process and a second variable for auxiliary decision-making;

[0049] An energy optimization module is used to construct and solve a third objective function based on the condition that the predicted growth data is satisfied, so as to obtain energy consumption and energy power requirements during the greening process;

[0050] A greening processing module is used to generate a greening strategy for the vegetation to be greened based on the first variable, the second variable, the energy consumption and the energy power demand, and perform vertical greening processing on the road based on the greening strategy.

[0051] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing a method for intelligent vertical greening of roads as described above.

[0052] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, a method for intelligent vertical greening of roads as described above is implemented.

[0053] The beneficial effects of the present invention are:

[0054] (1) By constructing a first objective function and building a growth prediction model based on algorithms such as support vector machines, the present invention can accurately predict the growth of green vegetation. Taking into account historical growth data and environmental factors, the model can accurately reflect future growth trends and provide a scientific basis for the formulation of greening strategies.

[0055] (2) The present invention generates an optimized greening strategy for the vegetation to be greened based on data such as the first variable (control measures), the second variable (decision support), energy consumption, and energy power requirements. Through simulation and optimization, the optimal control measures and energy management solutions are selected to maximize the greening effect and minimize energy consumption.

[0056] (3) The present invention aims to minimize energy consumption and energy power requirements through the optimization of the third objective function, thereby minimizing the use of energy resources while ensuring the greening effect. Effective energy management and energy-saving strategies can help reduce energy consumption and environmental pollution, and achieve sustainable development.

[0057] (4) The present invention combines machine learning algorithms and optimization methods to achieve intelligent management and decision-making of the greening process. Through real-time monitoring and data analysis, greening strategies and energy utilization plans can be adjusted in a timely manner, improving the adaptability and responsiveness of the greening system.

[0058] (5) The effective greening strategy of the present invention can not only promote vegetation growth and improve ambient air quality, but also beautify the urban environment and enhance the quality of life of residents. Reasonable greening design and management can help to create livable cities and eco-friendly communities.

[0059] In summary, the present invention can achieve dual optimization of road vegetation greening effects and energy utilization through scientific data analysis, model building and optimization methods, and has made positive contributions to urban ecological environment improvement and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A scene diagram of a road intelligent vertical greening method provided by the present invention;

[0061] Figure 2 A flow chart of a method for intelligent vertical greening of roads provided by the present invention;

[0062] Figure 3 A schematic diagram of the structure of an intelligent road vertical greening system provided by the present invention;

[0063] Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0064] Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0066] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0067] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0068] See also Figure 1 , Figure 1 This is a scene diagram of a road intelligent vertical greening method provided by the present invention. Figure 1As shown, the terminal and server are connected via a network, such as a wired or wireless network. Terminals include, but are not limited to, portable devices such as mobile phones and tablets installed with various network platform applications, as well as fixed devices such as computers, kiosks, and advertising machines. The server provides various business services to users, including service push servers and user recommendation servers.

[0069] It should be noted that Figure 1 The scene diagram of a method for intelligent vertical greening of roads shown is only an example. The terminal, server and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not generate any limitation on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0070] Among them, the terminal can be used to:

[0071] Obtain historical growth data and historical environmental data of vegetation to be greened on roads during historical planting;

[0072] constructing and solving a first objective function based on the historical growth data and the historical environmental data to obtain model parameters of a growth prediction model;

[0073] Predicting predicted growth data of the vegetation to be greened according to the growth prediction model;

[0074] Based on the condition that the predicted growth data is satisfied, a second objective function is constructed and solved to obtain a first variable of the control measure in the greening process and a second variable for auxiliary decision-making;

[0075] Based on the condition that the predicted growth data is satisfied, a third objective function is constructed and solved to obtain energy consumption and energy power requirements during the greening process;

[0076] Based on the first variable, the second variable, the energy consumption and the energy power demand, a greening strategy for the vegetation to be greened is generated, and vertical greening processing is performed on the road based on the greening strategy.

[0077] See also Figure 2 , provides a flow chart of a road intelligent vertical greening method of the present invention, comprising the following steps:

[0078] Step 201: Obtain historical growth data and historical environmental data of vegetation to be greened on the road during historical planting.

[0079] In some embodiments, the scope and type of vegetation to be greened on the road can be determined first, and the specific area and vegetation type for which data collection is required can be determined. For example, a certain road or a specific green area can be selected as the data collection target.

[0080] In some embodiments, appropriate data sources may be selected for data collection based on the determined scope and requirements, which may include historical greening records, environmental monitoring site data, meteorological data, soil data, and other data sources.

[0081] In some embodiments, historical growth data of vegetation to be greened on the road can be obtained, including but not limited to growth cycle: growth cycle and seasonal characteristics of different vegetation; growth indicators: growth height, density, number of leaves and other growth indicators; growth rate: changes in growth rate in different seasons or years; growth conditions: historical records of growth under environmental conditions such as temperature, humidity, and light.

[0082] In some embodiments, historical environmental data related to the growth of vegetation to be greened can be obtained, including but not limited to meteorological data: historical temperature, humidity, precipitation, wind speed and other meteorological data; light data: historical light intensity, sunshine duration and other data; soil data: soil moisture, soil nutrient content and other data; environmental quality data: air quality, PM2.5 and other environmental quality data; other environmental factors: may also include factors that affect vegetation growth, such as obstruction by surrounding buildings and traffic conditions.

[0083] In some embodiments, the collected historical growth data and historical environmental data may be cleaned and integrated to address issues such as outliers and missing values in the data, thereby ensuring the quality and integrity of the data.

[0084] In some embodiments, data analysis may be performed to extract features closely related to the growth of vegetation to be greened from historical data, such as seasonal changes, the degree of influence of environmental conditions on growth, and other features.

[0085] In some embodiments, the cleaned and integrated data can be stored and managed to establish a database or data warehouse of historical data to facilitate subsequent model training and greening strategy formulation.

[0086] Through the above steps, the historical growth data and historical environmental data of the vegetation to be greened on the road during historical planting can be obtained, providing basic support for subsequent data analysis, model building and greening strategy formulation.

[0087] In some embodiments, step 201 may include:

[0088] Acquiring initial data of the historical growth data and the historical environmental data;

[0089] Preprocessing the initial data to obtain preprocessed data;

[0090] Performing feature extraction on the preprocessed data to obtain feature data;

[0091] The characteristic data is subjected to data standardization processing to obtain the historical growth data and the historical environment data.

[0092] In some embodiments, the initial data for historical growth data may come from a dataset of past monitoring and recording of green vegetation, including information such as vegetation height, density, number of leaves, and growth cycle. The initial data for historical environmental data may include historical environmental data related to the green area, such as meteorological data (temperature, humidity, precipitation, wind speed, etc.), lighting data, soil data (humidity, nutrient content, etc.), and environmental quality data (air quality, PM2.5, etc.).

[0093] In some embodiments, preprocessing can include data cleaning, data conversion, and data integration. In some embodiments, outliers, missing values, and duplicate values in the data can be processed to ensure data quality and integrity. Data can also be converted, such as time format conversion and data type conversion, to integrate data from different sources into a unified data set for subsequent processing and analysis.

[0094] In some embodiments, features related to greening growth and environment, such as seasonal changes, meteorological conditions, soil characteristics, etc., can be selected from the preprocessed data, and the selected features can be combined or new features can be constructed to better reflect the correlation between growth and environment.

[0095] In some embodiments, the selected features may be standardized so that the data have the same scale and range, such as a mean of 0 and a variance of 1, or are normalized to a specific range.

[0096] Through the above steps, the present invention can extract characteristic data related to greening growth and environment from the initial data, and standardize this characteristic data to obtain historical growth data and historical environmental data for analysis and modeling. These data processing steps help improve data quality and analysis accuracy, providing a reliable foundation for subsequent greening strategy formulation and model training.

[0097] Step 202: construct and solve a first objective function based on the historical growth data and the historical environmental data to obtain model parameters of a growth prediction model.

[0098] In some embodiments, step 202 may include:

[0099] obtaining an initial growth prediction model;

[0100] Training the initial growth prediction model based on the training samples composed of the historical growth data and the historical environmental data, constructing the first objective function, and optimizing model parameters of the initial growth prediction model; the model parameters include a first weight, a bias term, and a slack variable;

[0101] Determine the optimized first weight, the bias term, and the slack variable to solve the first objective function.

[0102] In some embodiments, the first objective function can be expressed as:

[0103] ;

[0104] Among them, J is the first objective function, is the first weight, is the slack variable, Q is the regularization matrix, C is the penalty parameter, λ is the weight of the time factor, and b is the slack variable. and the bias term obtained by the penalty parameter C.

[0105] In the specific implementation, J is the first objective function, which represents the goal to be minimized, that is, the core goal of the optimization problem; is the first weight, one of the parameters in the support vector machine model, used to determine the direction and slope of the decision boundary. b is the bias term, also one of the parameters in the support vector machine model, used to adjust the position of the decision boundary; is a slack variable used to handle samples that cannot be perfectly classified by the model. It allows some samples to be on the wrong side of the decision boundary, but they need to be penalized (through the penalty parameter 𝐶 C Control); Q is the regularization matrix, which usually represents the correlation matrix of data features. Regularization term It is used to control the complexity of the model and prevent overfitting; C is a penalty parameter that controls the tolerance for misclassified samples. Increasing 𝐶 can make the model pay more attention to the accuracy of classification, but it may also lead to overfitting; λ is the weight parameter of the time factor, which is used to control the importance of the time factor term. Can be used to constrain or penalize the temporal characteristics of samples; It is a regularization term. By minimizing this term, the model can be made simpler and overfitting can be prevented. It is a penalty term. By minimizing this term, the model can avoid misclassification of samples as much as possible during training and improve the generalization ability of the model. It is the time factor term, which can constrain or punish the temporal characteristics of the sample. Adjusting 𝜆 according to the actual situation can adjust the emphasis on the time factor.

[0106] It can be understood that the goal of SVM is to find an optimal hyperplane that can separate sample points of different categories and maximize the separation boundary, thereby achieving the ability to generalize and predict new samples. The decision function of SVM can be expressed as: Among them, 𝑥 is the input feature vector, 𝜔 is the weight vector, 𝑏 is the bias term, 𝑠𝑖𝑔𝑛() is the symbolic function, according to The positive or negative value of determines the category of the data point. As you can understand, the key to SVM is the support vector, which is the data point closest to the separating hyperplane. These support vectors determine the position and direction of the separating hyperplane.

[0107] In the support vector machine (SVM) algorithm, the initial growth prediction model can select a linear kernel function or other suitable kernel function. The model consists of a weight vector ω, a bias term b, and a slack variable ξ.

[0108] Historical growth data and environmental data can be used to form a training sample set. Each sample contains growth characteristics (such as height and density), environmental characteristics (such as temperature and humidity), and possibly a time factor. Each sample also includes information such as the corresponding growth state label or growth rate, which serves as a label for the training set.

[0109] Based on the concept of Support Vector Machine (SVM), an objective function J is constructed, which can include, for example, a regularization term, a penalty term, and a time factor term. The goal is to learn the appropriate weight vector ω, bias term b, and slack variable ξ by minimizing J to accurately predict the growth state or growth rate of the vegetation to be greened.

[0110] In some embodiments, a training set of samples can be used to train the model, optimize the objective function J, and solve for the optimal weight vector ω, bias term b, and slack variable ξ. During training, the model's complexity and fitting ability can be controlled by adjusting the regularization parameter C, penalty parameter λ, and other hyperparameters.

[0111] After training and optimization, the optimal weight vector ω, bias term b, and slack variable ξ are obtained. These parameters constitute the optimized initial growth prediction model. These parameters reflect the model's fit to historical data and its ability to generalize to unknown data.

[0112] The optimized parameters can be substituted into the first objective function J, and the model's predictive and generalization capabilities can be verified by minimizing J. In practical applications, the model can be evaluated through methods such as cross-validation to ensure its reliability and stability.

[0113] Through the above steps, the present invention can obtain an initial growth prediction model, and obtain suitable model parameters through training and optimization, thereby establishing a prediction model for the growth of green vegetation, providing an important basis for the subsequent greening strategy formulation.

[0114] Step 203: predicting the predicted growth data of the vegetation to be greened according to the growth prediction model.

[0115] In some embodiments, a training sample set can be constructed using historical growth data and historical environmental data, and a growth prediction model can be trained based on these sample sets. This model can be a machine learning algorithm such as a support vector machine (SVM).

[0116] The vegetation to be greened is extracted and normalized as the input feature vector x. The model parameters ω and bias term b obtained during the training process will be used to construct the decision function f(x), where This decision function determines the growth status of the vegetation to be greened based on the input feature vector x.

[0117] The output of the decision function f(x) can be used to obtain predicted growth data for the vegetation to be greened. If the output of the decision function is positive, it means that the model predicts that the vegetation will have a good growth state; if it is negative, it means that the model predicts that the vegetation growth may be affected by some restrictions or unfavorable factors.

[0118] As an example only, for vegetation A, the historical growth data can be the growth data of vegetation A in a certain area in the past year, including the average growth height and average growth rate per month, etc. The historical environmental data can be the environmental data such as temperature, humidity, light intensity, etc. in the area in the past year, as well as other possible influencing factors.

[0119] In some embodiments, a machine learning algorithm such as a support vector machine (SVM) can be used to train a growth prediction model based on the above historical data. The model learns the relationship between the growth characteristics of vegetation A and environmental factors. For example, if you want to predict the growth of vegetation A next month, you can collect real-time data as input feature vectors: real-time environmental data such as temperature, humidity, and light intensity of the current month. Characteristic data such as the type and planting density of vegetation A. Real-time data can be input into the trained growth prediction model, and the model calculates the predicted value through a decision function. The model prediction result is: the predicted average growth height of vegetation A next month is 30 cm, which means that based on the current environmental conditions and planting conditions, the average height of vegetation A is expected to reach about 30 cm.

[0120] This result can help adjust management measures such as irrigation and nutrient supply to better promote plant growth. If the predicted results deviate from the actual growth, the model can be further optimized or the management strategy can be adjusted.

[0121] Step 204: Based on the condition that the predicted growth data is satisfied, a second objective function is constructed and solved to obtain a first variable of the control measure in the greening process and a second variable for auxiliary decision-making.

[0122] In some embodiments, step 204 may include:

[0123] Acquiring a plurality of initial first variables for predicting control measures in the greening process and a plurality of initial second variables for assisting decision-making;

[0124] Obtaining a first weight of each of the initial first variables and a second weight of each of the initial second variables;

[0125] Obtaining a third weight between each of the initial first variables and each of the initial second variables;

[0126] Based on the first weight, the second weight, and the third weight, performing weighted summation on a plurality of the first variables and a plurality of the second variables to obtain a first sum value;

[0127] The second objective function is constructed and solved based on the first sum value.

[0128] In some embodiments, the second objective function is expressed as:

[0129] ;

[0130] Among them, f is the second objective function, x is the first variable, y is the second variable, is the first weight, v is the second weight, is the initial first variable of the i-th control measure, represents the initial second variable of the j-th auxiliary decision, n is the total number of initial first variables, and m is the total number of initial second variables.

[0131] In the specific implementation, is the initial first variable of the i-th control measure, such as irrigation amount, fertilizer amount, temperature adjustment, etc. Represents the set point or intensity of a control measure; Represents the initial second variable of the j-th auxiliary decision, such as light intensity, wind speed, etc. Represents the set value or intensity of an auxiliary decision factor; is the first weight of the i-th control measure, indicating its importance to vegetation growth, e.g. Indicates the importance of irrigation amount, Indicates the importance of fertilizer application amount, and so on; is the second weight of the jth auxiliary decision variable, indicating the importance of the corresponding auxiliary factor, for example Indicates the importance of light intensity, Indicates the importance of humidity, and so on; The third weight of the interaction between the initial first variable and the initial second variable can control the degree of influence of this influence relationship on the objective function; the goal is to find an optimal set of first variables x and second variables y so that the weighted sum is minimized, thereby meeting the needs of vegetation growth to the greatest extent.

[0132] It can be understood that the goal of the second objective function is to find the optimal set of values for the first and second variables by optimizing the second objective function to minimize the weighted sum, thereby maximally meeting the needs of vegetation growth. This weighted sum includes the impact of control measures, the influence of auxiliary factors, and the interactions between them. This helps us systematically consider various factors in the greening process and find the optimal management strategy to promote vegetation growth and improve the ecological environment.

[0133] Step 205: Based on the condition that the predicted growth data is satisfied, a third objective function is constructed and solved to obtain the energy consumption and energy power demand during the greening process.

[0134] In some embodiments, step 205 may include:

[0135] Obtaining a plurality of initial energy consumptions, a plurality of initial energy unit area consumptions, and a plurality of initial energy power requirements corresponding to a plurality of energy sources predicted during the greening process;

[0136] obtaining a fourth weight of each of the initial energy consumption amounts and a fifth weight of each of the initial energy power requirements;

[0137] The third objective function is constructed and solved based on the fourth weight, the second weight, the third weight, the multiple initial energy consumption amounts, the multiple initial energy unit area consumptions and the multiple initial energy power requirements.

[0138] In some embodiments, the third objective function can be expressed as:

[0139] ;

[0140] in, is the third objective function, represents the jth initial energy consumption, represents the jth initial energy consumption per unit area, represents the jth initial energy power requirement, It is the fourth weight of total energy consumption. The fifth weight representing the energy power requirement, j is a serial number, and the total of the initial energy consumption is equal to the total of the initial energy power requirement, both of which are q.

[0141] In the specific implementation, The third objective function has two goals: minimizing the sum of squares of energy consumption per unit area, that is, pursuing the maximization of energy utilization efficiency; minimizing the total energy consumption, that is, pursuing the minimization of overall energy consumption; represents the jth type of initial energy consumption, such as electrical energy, water energy, thermal energy, etc.; Represents the j-th initial energy consumption per unit area.

[0142] In some embodiments, represents the jth initial energy power requirement, which can include, for example, electrical energy, hydraulic energy, thermal energy, and other energy requirements. Specifically, assume that a system includes an electrically powered device, such as a water pump or LED lighting system, that requires electrical energy to operate. The power requirement of these devices can be expressed as the amount of electrical energy consumed per hour, typically measured in kilowatt-hours (kWh). If the system includes a water pump or irrigation system, which requires hydraulic energy to move water, the pump's power requirement can be determined by flow rate and pressure, for example, how many cubic meters of water must be pumped per hour and how much power is required to achieve the required pressure. Devices or systems that require temperature stability require thermal energy to achieve this. For example, a greenhouse system may require heating equipment to regulate the temperature, which results in a certain thermal energy power requirement. Depending on the specific system design and operational requirements, other energy sources, such as wind and solar energy, may also be involved. Each energy source has a corresponding power requirement to meet the system's operational and functional requirements.

[0143] In some embodiments, It is the fourth weight of energy consumption, used to balance the two goals of efficiency and total amount; The fifth weight representing the energy power demand, j is a number value, the total amount of the initial energy consumption is equal to the total amount of the initial energy power demand, both of which are q; by optimizing this objective function, the energy consumption of the intelligent vertical greening system can be minimized while meeting the vegetation growth requirements.

[0144] It's understandable that solving the third objective function aims to minimize the sum of the squares of energy consumption per unit area, maximizing energy efficiency. Minimizing total energy consumption also minimizes overall energy consumption. By optimizing this objective function, we can minimize the energy consumption of the intelligent vertical greening system while meeting the needs of vegetation growth, achieving a balance between energy efficiency and total energy consumption.

[0145] Step 206: Generate a greening strategy for the vegetation to be greened based on the first variable, the second variable, the energy consumption, and the energy power demand, and perform vertical greening on the road based on the greening strategy.

[0146] In some embodiments, step 206 may include:

[0147] Acquiring initial data of the historical growth data and the historical environmental data;

[0148] Preprocessing the initial data to obtain preprocessed data;

[0149] Performing feature extraction on the preprocessed data to obtain feature data;

[0150] The characteristic data is subjected to data standardization processing to obtain the historical growth data and the historical environment data.

[0151] In some embodiments, the optimized first and second variables can be used as control measures to perform greening operations on the vegetation within a target period based on actual energy consumption and power requirements. During the greening process, it is necessary to monitor and record actual energy consumption and power requirements, as well as key data such as real-time environmental conditions and growth status.

[0152] In some embodiments, phased greening results can be obtained based on monitoring data and actual growth conditions during the greening process, including data such as vegetation growth conditions, energy consumption, and power requirements.

[0153] In some embodiments, phased greening results can be analyzed to assess factors such as vegetation growth, energy efficiency, and energy consumption. Based on the analysis results, the first and second variables are adjusted to optimize control measures, ensuring that the greening strategy better meets actual needs and energy conservation and emission reduction requirements. Furthermore, based on actual energy consumption and power demand data, energy utilization strategies can be adjusted to improve energy efficiency and reduce total energy consumption.

[0154] Through the above methods, the present invention can achieve effective management of green vegetation and optimized regulation of the greening process, thereby realizing a more energy-saving and environmentally friendly intelligent vertical greening system.

[0155] See also Figure 3 , Figure 3 This is a structural schematic diagram of a road intelligent vertical greening system provided by the present invention.

[0156] like Figure 3 As shown, an intelligent road vertical greening system proposed in an embodiment of the present invention includes:

[0157] The data acquisition module 301 is used to acquire historical growth data and historical environmental data of the vegetation to be greened on the road during historical planting;

[0158] A parameter determination module 302 is configured to construct and solve a first objective function based on the historical growth data and the historical environmental data to obtain model parameters of a growth prediction model;

[0159] A growth prediction module 303 is configured to predict the predicted growth data of the vegetation to be greened according to the growth prediction model;

[0160] The variable determination module 304 is used to construct and solve the second objective function based on the condition that the predicted growth data is satisfied, and obtain the first variable of the control measure in the greening process and the second variable for auxiliary decision-making;

[0161] The energy optimization module 305 is used to construct and solve the third objective function based on the condition that the predicted growth data is satisfied, and obtain the energy consumption and energy power demand during the greening process;

[0162] The greening processing module 306 is used to generate a greening strategy for the vegetation to be greened based on the first variable, the second variable, the energy consumption and the energy power demand, and perform vertical greening processing on the road based on the greening strategy.

[0163] See also Figure 4 , Figure 4 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0164] Obtain historical growth data and historical environmental data of vegetation to be greened on roads during historical planting;

[0165] constructing and solving a first objective function based on the historical growth data and the historical environmental data to obtain model parameters of a growth prediction model;

[0166] Predicting predicted growth data of the vegetation to be greened according to the growth prediction model;

[0167] Based on the condition that the predicted growth data is satisfied, a second objective function is constructed and solved to obtain a first variable of the control measure in the greening process and a second variable for auxiliary decision-making;

[0168] Based on the condition that the predicted growth data is satisfied, a third objective function is constructed and solved to obtain energy consumption and energy power requirements during the greening process;

[0169] Based on the first variable, the second variable, the energy consumption and the energy power demand, a greening strategy for the vegetation to be greened is generated, and vertical greening processing is performed on the road based on the greening strategy.

[0170] See also Figure 5 , Figure 5 Schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0171] Obtain historical growth data and historical environmental data of vegetation to be greened on roads during historical planting;

[0172] constructing and solving a first objective function based on the historical growth data and the historical environmental data to obtain model parameters of a growth prediction model;

[0173] Predicting predicted growth data of the vegetation to be greened according to the growth prediction model;

[0174] Based on the condition that the predicted growth data is satisfied, a second objective function is constructed and solved to obtain a first variable of the control measure in the greening process and a second variable for auxiliary decision-making;

[0175] Based on the condition that the predicted growth data is satisfied, a third objective function is constructed and solved to obtain energy consumption and energy power requirements during the greening process;

[0176] Based on the first variable, the second variable, the energy consumption and the energy power demand, a greening strategy for the vegetation to be greened is generated, and vertical greening processing is performed on the road based on the greening strategy.

[0177] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0178] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0179] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0180] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0182] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0183] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for intelligent vertical greening of roads, characterized in that: The method comprises: Obtain historical growth data and historical environmental data of vegetation to be greened on roads during historical planting; Constructing and solving a first objective function based on the historical growth data and the historical environmental data to obtain model parameters of a growth prediction model, including: obtaining an initial growth prediction model; training the initial growth prediction model based on a training sample consisting of the historical growth data and the historical environmental data, constructing the first objective function, and optimizing model parameters of the initial growth prediction model; the model parameters including a first weight, a bias term, and a slack variable; and determining the optimized first weight, the bias term, and the slack variable to solve the first objective function; Predicting predicted growth data of the vegetation to be greened according to the growth prediction model; Based on the condition that the predicted growth data is satisfied, a second objective function is constructed and solved to obtain a first variable of the control measure in the greening process and a second variable for auxiliary decision-making, including: obtaining a plurality of initial first variables of the control measure predicted in the greening process and a plurality of initial second variables for auxiliary decision-making; obtaining a first weight of each of the initial first variables and a second weight of each of the initial second variables; obtaining a third weight between each of the initial first variables and each of the initial second variables; based on the first weight, the second weight and the third weight, performing a weighted summation of the plurality of first variables and the plurality of second variables to obtain a first sum value; and constructing and solving the second objective function based on the first sum value; Based on the condition that the predicted growth data is satisfied, a third objective function is constructed and solved to obtain the energy consumption and energy power demand in the greening process, including: obtaining multiple initial energy consumptions, multiple initial energy unit area consumptions, and multiple initial energy power demands corresponding to the multiple energy sources predicted in the greening process; obtaining a fourth weight for each of the initial energy consumptions, and a fifth weight for each of the initial energy power demands; constructing and solving the third objective function based on the fourth weight, the second weight, the third weight, the multiple initial energy consumptions, the multiple initial energy unit area consumptions, and the multiple initial energy power demands; Based on the first variable, the second variable, the energy consumption and the energy power demand, a greening strategy for the vegetation to be greened is generated, and vertical greening processing is performed on the road based on the greening strategy.

2. The road intelligent vertical greening method according to claim 1, characterized in that: The first objective function is expressed as: Among them, J is the first objective function, ω is the first weight, ξ i is the slack variable, Q is the regularization matrix, C is the penalty parameter, λ is the weight of the time factor, and b is the slack variable ξ i and the bias term obtained by the penalty parameter C.

3. The road intelligent vertical greening method according to claim 2, characterized in that: The second objective function is expressed as: Among them, f is the second objective function, x is the first variable, y is the second variable, ω is the first weight, v is the second weight, x i is the initial first variable of the i-th control measure, y j represents the initial second variable of the j-th auxiliary decision, n is the total number of initial first variables, and m is the total number of initial second variables.

4. The method for intelligent vertical greening of roads according to claim 3, characterized in that: The third objective function is expressed as: Among them, g is the third objective function, E j represents the jth initial energy consumption, A j represents the jth initial energy consumption per unit area, P j represents the jth initial energy power demand, γ is the fourth weight of the total energy consumption, δ represents the fifth weight of the energy power demand, j is a serial value, and the total amount of the initial energy consumption is equal to the total amount of the initial energy power demand, both of which are q.

5. The method for intelligent vertical greening of roads according to claim 4, characterized in that: The generating of a greening strategy for the vegetation to be greened based on the first variable, the second variable, the energy consumption, and the energy power demand includes: Greening the vegetation to be greened within a target period using the first variable, the second variable, the energy consumption, and the energy power demand to obtain a phased greening result; Based on the phased greening results, the first variable, the second variable, the energy consumption and the energy power demand are adjusted to obtain the greening strategy.

6. The method for intelligent vertical greening of roads according to any one of claims 1 to 5, characterized in that: The acquisition of historical growth data and historical environmental data of the vegetation to be greened on the road during historical planting includes: Acquiring initial data of the historical growth data and the historical environmental data; Preprocessing the initial data to obtain preprocessed data; Performing feature extraction on the preprocessed data to obtain feature data; The characteristic data is subjected to data standardization processing to obtain the historical growth data and the historical environment data.

7. A road intelligent vertical greening system, characterized in that: The system comprises: A data acquisition module is used to obtain historical growth data and historical environmental data of the vegetation to be greened on the road during historical planting; a parameter determination module, configured to construct and solve a first objective function based on the historical growth data and the historical environmental data to obtain model parameters of a growth prediction model, and further configured to obtain an initial growth prediction model; train the initial growth prediction model based on a training sample composed of the historical growth data and the historical environmental data, construct the first objective function, and optimize the model parameters of the initial growth prediction model; the model parameters including a first weight, a bias term, and a slack variable; and determine the optimized first weight, the bias term, and the slack variable to solve the first objective function; A growth prediction module, configured to predict the predicted growth data of the vegetation to be greened according to the growth prediction model; a variable determination module, configured to construct and solve a second objective function based on the condition that the predicted growth data is satisfied, obtain a first variable for a control measure in the greening process, and a second variable for auxiliary decision-making, and further configured to obtain a plurality of initial first variables for the control measure predicted in the greening process, and a plurality of initial second variables for auxiliary decision-making; obtain a first weight for each of the initial first variables, and a second weight for each of the initial second variables; obtain a third weight between each of the initial first variables and each of the initial second variables; based on the first weight, the second weight, and the third weight, perform a weighted sum of a plurality of the first variables and a plurality of the second variables to obtain a first sum value; and construct and solve the second objective function based on the first sum value; An energy optimization module is used to construct and solve a third objective function based on the condition that the predicted growth data is satisfied, to obtain the energy consumption and energy power demand during the greening process, and is also used to obtain multiple initial energy consumptions, multiple initial energy unit area consumptions, and multiple initial energy power demands corresponding to multiple energy sources predicted during the greening process; obtain a fourth weight for each of the initial energy consumptions, and a fifth weight for each of the initial energy power demands; and construct and solve the third objective function based on the fourth weight, the second weight, the third weight, the multiple initial energy consumptions, the multiple initial energy unit area consumptions, and the multiple initial energy power demands; A greening processing module is used to generate a greening strategy for the vegetation to be greened based on the first variable, the second variable, the energy consumption and the energy power demand, and perform vertical greening processing on the road based on the greening strategy.

Images