Shore protection forest ecological monitoring and maintenance station integrating solar power supply

Abstract

The invention discloses a bank protection forest ecological monitoring and maintenance station integrating solar power supply. The bank protection forest ecological monitoring and maintenance station comprises a solar module, an ecological monitoring module and a maintenance module, the solar module converts solar energy into electric energy through a solar cell panel and supplies power to the ecological monitoring module and the maintenance module. The ecological monitoring module comprises a sensor system and is used for monitoring ecological environment parameters of the bank protection forest in real time; the image acquisition system is used for shooting and recording the state change of the bank protection forest in real time; the ecological monitoring module performs fitting on the ecological environment parameters and the state change to obtain a real-time state of the bank protection forest, and then compares the real-time state with an ideal state to obtain a state difference; the maintenance module receives a state difference sent by the ecological environment parameters and maintains the bank protection forest; the ecological environment and state changes of the bank protection forest are monitored in real time through the sensor system and the image acquisition system, problems are found and solved in time, and the management efficiency and ecological benefits of the bank protection forest are improved.

Description

Technical Field

[0001] The invention relates to a bank protection forest ecological monitoring and maintenance technology, and in particular to a bank protection forest ecological monitoring and maintenance station integrated with solar energy power supply. Background Art

[0002] Bank protection forests are protective forests planted on both sides of channels and rivers to prevent erosion. Bank protection forests play an important role in preventing soil erosion, protecting riverbank stability, and maintaining ecological balance. They can not only reduce the impact of floods, but also accumulate silt, gradually narrow the width of the river channel, and gradually straighten the river channel from a bend. In addition, bank protection forests can improve the climate conditions in the basin, improve the quality of the landscape, and provide a good place for leisure and entertainment for surrounding residents.

[0003] However, the existing bank protection forest ecological monitoring and maintenance technologies have many shortcomings. For example, many bank protection forest ecological monitoring and maintenance systems rely on external power grids for power supply. This approach will cause great limitations in remote areas or areas where the power grid is not covered, because these areas are often difficult to provide a stable power supply. The instability of energy supply leads to interruptions in monitoring and maintenance work, affecting the continuity of bank protection forest management.

[0004] In addition, traditional monitoring methods usually rely on manual inspections or single sensor monitoring. Manual inspections are time-consuming and labor-intensive, and it is difficult to obtain data in real time; while single sensor monitoring cannot fully and accurately reflect the ecological environment and state changes of the revetment forest, which leads to misjudgment or omission of the revetment forest status and affects the accuracy of management decisions. Due to the lack of intelligent decision support systems and precise maintenance methods, the maintenance of revetment forests is often delayed and blind, which leads to untimely and inadequate maintenance work, and may even aggravate the ecological problems of the revetment forests.

[0005] Therefore, there is an urgent need for an integrated solar-powered bank protection forest ecological monitoring and maintenance station to solve the technical problems existing in the above-mentioned prior art. Summary of the invention

[0006] The present invention overcomes the shortcomings of the prior art and provides a bank protection forest ecological monitoring and maintenance station which is powered by integrated solar energy.

[0007] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: an integrated solar-powered bank protection forest ecological monitoring and maintenance station, comprising: a solar module, an ecological monitoring module and a maintenance module; the solar module uses a solar panel to convert solar energy into electrical energy to provide power supply for the ecological monitoring module and the maintenance module;

[0008] The ecological monitoring module includes a sensor system for real-time monitoring of the ecological environment parameters of the bank protection forest; and also includes an image acquisition system for real-time recording and recording the state changes of the bank protection forest; the ecological monitoring module fits the ecological environment parameters with the state changes to obtain the real-time state of the bank protection forest, and then compares the real-time state with the ideal state to obtain the state difference;

[0009] The maintenance module receives the state difference sent from the ecological environment parameter, and uses the automatic irrigation system, temperature system and lighting system to maintain the bank protection forest until the state difference is eliminated.

[0010] In a preferred embodiment of the present invention, the sensor system includes a temperature sensor, a humidity sensor, a light sensor and a soil moisture sensor. The ecological monitoring module adopts a hybrid data fusion method to fuse the data monitored by the sensor system, and uses a Bayesian inference algorithm to extract features from the fused data to obtain the ecological environment parameters.

[0011] In a preferred embodiment of the present invention, the process of acquiring the ecological environment parameters includes the following steps:

[0012] S1, obtain the raw data of temperature, humidity, light, and soil moisture from the sensor system, and clean the raw data to remove noise and outliers;

[0013] S2. Setting a unified timestamp in the sensor system for time synchronization between sensors in the sensor system;

[0014] S3, normalize the data from different sensors and convert the data to a unified scale;

[0015] S4. Using the Bayesian inference algorithm, extracting data features based on the probability principle, processing information with uncertainty, and obtaining the ecological environment parameters; wherein the formula expression of the Bayesian inference algorithm is:

[0016]

[0017] Where P(H|E) represents the posterior probability that the hypothesis H is true given the observed data E, P(E|H) represents the likelihood probability that the observed data E appears given the hypothesis H is true, P(H) represents the prior probability that the hypothesis H is true, and P(E) represents the marginal probability that the observed data E appears;

[0018] The fixed observation data E refers to the actual measurement value collected by the sensor system. Therefore, E is a set of multiple sensor measurement values, specifically: E = {E T ,E H ,EL ,E S}; where E T E is the temperature value measured by the temperature sensor; H The humidity value measured by the humidity sensor; E L The light intensity value measured by the light sensor; E S The soil moisture content value measured by the soil moisture sensor.

[0019] In a preferred embodiment of the present invention, the real-time state fitting process includes the following steps:

[0020] A1. Extract key features that can reflect the state of the bank protection forest from ecological environment parameters; extract image features that can reflect the changes in the state of the bank protection forest from the image acquisition system;

[0021] A2. Use machine learning methods to construct a single linear fitting model, use the key features and image features in step A1 as data input, and train the single linear fitting model. During the training process, the single linear fitting model learns the relationship between the ecological environment parameters and the state changes of the bank protection forest, and obtains the change law;

[0022] A3. The single linear fitting model outputs the real-time status of the bank protection forest according to the input characteristics and change rules. In a preferred embodiment of the present invention, the formula expression of the single linear fitting model is:

[0023] Z=β0+β1x1y1+β2x2y2+…+β n x n y n +ε

[0024] Where Z is the real-time status of the bank protection forest; x1, x2...x n are the extracted key features; y1, y2...y n are the extracted image features; β0, β1, β2...β n are the parameters of the single linear fitting model, where β0+β1+β2+…+β n =1; ε is the error term, which represents the difference between the model predicted value and the actual value.

[0025] In a preferred embodiment of the present invention, the ideal state is obtained based on actual management experience and historical data of the bank protection forest, taking into account the performance of the bank protection forest in different seasons and different climatic conditions and the impact of human activities on it.

[0026] In a preferred embodiment of the present invention, the maintenance module also includes an intelligent decision-making system, which automatically formulates a maintenance strategy according to the size and nature of the state difference and controls the operation of the automatic irrigation system, temperature system and lighting system.

[0027] In a preferred embodiment of the present invention, it also includes a remote communication module for remotely transmitting the real-time status obtained by the ecological monitoring module, the working status of the maintenance module and the power supply status of the solar module to a monitoring center or a mobile terminal. Users can understand the ecological status and maintenance status of the bank protection forest in real time, and conduct remote monitoring and management.

[0028] In a preferred embodiment of the present invention, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned implementation steps of the bank protection forest ecological monitoring and maintenance station when executing the computer program.

[0029] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the implementation steps of the above-mentioned bank protection forest ecological monitoring and maintenance station.

[0030] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0031] (1) Using solar energy for power supply, no external power supply is required, which reduces energy consumption and operating costs, and also reduces pollution to the environment.

[0032] (2) The sensor system and image acquisition system are used to monitor the ecological environment and status changes of the bank protection forest in real time, so that problems can be discovered and solved in a timely manner, thereby improving the management efficiency and ecological benefits of the bank protection forest.

[0033] (3) Intelligent decision-making systems and machine learning methods are used to achieve intelligent management of bank protection forests, improving the accuracy and efficiency of maintenance.

[0034] (4) Through the remote communication module, users can understand the ecological status and maintenance of the bank protection forest in real time and conduct remote monitoring and management, which improves the convenience and flexibility of management. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 is a flow chart of the bank protection forest ecological monitoring and maintenance station system of the preferred embodiment of the present invention;

[0037] Figure 2 is a flow chart of the process of obtaining ecological environment parameters according to a preferred embodiment of the present invention;

[0038] Figure 3 It is a flow chart of the fitting process of the real-time state of the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0041] like Figure 1 , Figure 2 and Figure 3 As shown, an integrated solar-powered bank protection forest ecological monitoring and maintenance station includes: a solar module, an ecological monitoring module and a maintenance module; the solar module uses a solar panel to convert solar energy into electrical energy to provide power supply for the ecological monitoring module and the maintenance module;

[0042] Furthermore, the sensor system includes a temperature sensor, a humidity sensor, a light sensor and a soil moisture sensor. The ecological monitoring module adopts a hybrid data fusion method to fuse the data monitored by the sensor system, and uses a Bayesian reasoning algorithm to extract features from the fused data to obtain ecological environment parameters.

[0043] The ecological monitoring module includes a sensor system for real-time monitoring of the ecological environment parameters of the bank protection forest; it also includes an image acquisition system for real-time recording of the state changes of the bank protection forest; the ecological monitoring module fits the ecological environment parameters with the state changes to obtain the real-time state of the bank protection forest, and then compares the real-time state with the ideal state to obtain the state difference;

[0044] Further, such as Figure 2 As shown in Figure 1, the process of obtaining ecological environment parameters includes the following steps:

[0045] S1, obtain the raw data of temperature, humidity, light, and soil moisture from the sensor system, and clean the raw data to remove noise and outliers;

[0046] S2. Setting a unified timestamp in the sensor system for time synchronization between sensors in the sensor system;

[0047] S3, normalize the data from different sensors and convert the data to a unified scale;

[0048] S4. Using the Bayesian inference algorithm, extract data features based on the probability principle, process uncertain information, and obtain ecological environment parameters; the formula of the Bayesian inference algorithm is:

[0049]

[0050] Where P(H|E) represents the posterior probability that the hypothesis H is true given the observed data E, P(E|H) represents the likelihood probability that the observed data E appears given the hypothesis H is true, P(H) represents the prior probability that the hypothesis H is true, and P(E) represents the marginal probability that the observed data E appears;

[0051] A further explanation is that P(H|E) is the posterior probability, which represents the probability that hypothesis H is true given the observed data E.

[0052] P(E|H) is the likelihood probability, which represents the probability of observing data E under the assumption that H is true.

[0053] P(H) is the prior probability, which indicates the probability that hypothesis H is true before any observation data is available.

[0054] P(E) is the marginal probability, which represents the total probability of observing data E and can usually be calculated by summing or integrating over all possible hypotheses.

[0055] The fixed observation data E refers to the actual measurement value collected by the sensor system. Therefore, E is a set of multiple sensor measurement values, specifically: E = {E T ,E H ,E L ,E S}; where E T E is the temperature value measured by the temperature sensor; H The humidity value measured by the humidity sensor; E L The light intensity value measured by the light sensor; E S The soil moisture content value measured by the soil moisture sensor.

[0056] Furthermore, if Figure 3As shown, the real-time state fitting process includes the following steps:

[0057] A1. Extract key features that can reflect the state of the bank protection forest from ecological environment parameters; extract image features that can reflect the changes in the state of the bank protection forest from the image acquisition system;

[0058] A2. Use machine learning methods to construct a single linear fitting model, use the key features and image features in step A1 as data input, and train the single linear fitting model. During the training process, the single linear fitting model learns the relationship between the ecological environment parameters and the state changes of the bank protection forest, and obtains the change law;

[0059] A3. The single linear fitting model outputs the real-time status of the bank protection forest according to the input characteristics and change rules. In a preferred embodiment, the formula expression of the single linear fitting model is:

[0060] Z=β0+β1x1y1+β2x2y2+…+β n x n y n +ε

[0061] Where Z is the real-time status of the bank protection forest; x1, x2...x n are the extracted key features; y1, y2...y n are the extracted image features; β0, β1, β2...β n are the parameters of the single linear fitting model, where β0+β1+β2+…+β n =1; ε is the error term, which represents the difference between the model predicted value and the actual value.

[0062] Furthermore, the parameters β0, β1, β2...β n It is solved by optimization algorithms such as least squares method, gradient descent method or stochastic gradient descent method to ensure the optimality of model parameters and minimize the difference between the predicted value and the actual value (i.e., the error term ε).

[0063] The ideal state is based on combining the actual management experience and historical data of the bank protection forest, taking into account the performance of the bank protection forest in different seasons and climatic conditions and the impact of human activities on it.

[0064] Furthermore, the ideal state should conform to ecological principles, including species diversity, ecological balance, continuity of ecological processes, etc. These principles provide a scientific basis for determining the health standards of the ecological environment of the bank protection forest.

[0065] Refer to relevant standards in environmental science, such as air quality standards, water quality standards, soil quality standards, etc., to ensure that various indicators of the ecological environment of the bank protection forest meet or exceed these standards.

[0066] Combining the actual management experience and historical data of bank protection forests, considering the performance of bank protection forests in different seasons and climatic conditions, as well as the impact of human activities on them, we can determine an ideal state that is both scientific and practical.

[0067] Experts in ecology, environmental science, forestry and other fields are invited to provide consultation and evaluation to ensure that the determination of the ideal state is authoritative and scientific.

[0068] The maintenance module receives the status difference sent from the ecological environment parameters, and uses the automatic irrigation system, temperature system and lighting system to maintain the bank protection forest until the status difference is eliminated.

[0069] The state difference refers to the difference between the real-time state of the bank protection forest and the ideal state, which is calculated by the following steps:

[0070] The sensor system and image acquisition system in the ecological monitoring module collect real-time ecological environment parameters and state change data of the bank protection forest. The data includes ecological environment parameters such as temperature, humidity, light intensity, soil moisture content, as well as state change data such as the growth status, pest and disease status, and vegetation coverage of the bank protection forest.

[0071] The collected data is cleaned, denoised and processed for outliers to ensure the accuracy and reliability of the data. At the same time, the data is normalized and converted to a unified scale for comparison and analysis.

[0072] Machine learning methods, such as single linear fitting models, polynomial regression models, support vector machine models or neural network models, are used to fit ecological environmental parameters with state change data to obtain the real-time status of the bank protection forest.

[0073] The ideal state of the ecological environment of the bank protection forest is determined based on ecological principles, environmental science standards and practical experience. The ideal state can be a specific numerical range or a comprehensive indicator or evaluation system.

[0074] Compare the real-time status of the revetment forest with the ideal status and calculate the difference between them. The difference can be measured by various indicators, such as temperature difference, humidity difference, light intensity difference, soil moisture content difference, etc. At the same time, comprehensive indicators or evaluation systems can also be used to evaluate the overall status difference.

[0075] Furthermore, the maintenance module also includes an intelligent decision-making system, which automatically formulates maintenance strategies according to the size and nature of the state difference and controls the operation of the automatic irrigation system, temperature system and lighting system.

[0076] It also includes a remote communication module, which is used to remotely transmit the real-time status obtained by the ecological monitoring module, the working status of the maintenance module and the power supply status of the solar module to the monitoring center or mobile terminal. Users can understand the ecological status and maintenance status of the bank protection forest in real time, and conduct remote monitoring and management.

[0077] Furthermore, the remote communication module also supports cloud computing and Internet of Things technologies, and can upload ecological monitoring data, maintenance records and other information to the cloud server to achieve remote storage, analysis and sharing of data. It also supports access and control of multiple mobile terminal devices, making it convenient for users to monitor and manage anytime and anywhere.

[0078] In a preferred embodiment, the maintenance module also includes a fault alarm system, which can automatically trigger the alarm mechanism when an abnormal state of the bank protection forest or a failure of the maintenance equipment is detected, and send an alarm message to the user through the remote communication module so that timely measures can be taken to deal with it.

[0079] In one embodiment, a computer device is provided, which may be a server. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data of a solar module, an ecological monitoring module, and a maintenance module. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an integrated solar-powered bank protection forest ecological monitoring and maintenance station is implemented.

[0080] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an integrated solar-powered bank protection forest ecological monitoring and maintenance station is implemented. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball, or touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0081] When the present invention is used, solar panels are used to convert solar energy into electrical energy to provide power supply for the entire system. In this way, the monitoring and maintenance station can be operated independently in areas far away from the power grid without external power support.

[0082] The sensor system includes temperature sensors, humidity sensors, light sensors and soil moisture sensors to monitor the ecological and environmental parameters of the bank protection forest in real time. The image acquisition system records the changes in the status of the bank protection forest in real time, such as vegetation growth, signs of pests and diseases, etc.

[0083] A hybrid data fusion method is used to fuse sensor data and image data, and feature extraction is performed through the Bayesian inference algorithm to obtain the ecological and environmental parameters of the bank protection forest. Then, a single linear fitting model is constructed using machine learning methods to combine the ecological and environmental parameters with image features to fit the real-time status of the bank protection forest.

[0084] The real-time state is compared with the preset ideal state, and the state difference, that is, the difference between the actual state and the ideal state, is calculated.

[0085] The maintenance module receives the status difference information from the ecological monitoring module. According to the size and nature of the status difference, the intelligent decision-making system automatically formulates a maintenance strategy and controls the automatic irrigation system, temperature system and lighting system to perform maintenance operations on the bank protection forest until the status difference is eliminated and the bank protection forest is restored to an ideal state.

[0086] The above is based on the ideal embodiment of the present invention. Through the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the content in the specification, and the technical scope must be determined according to the scope of the claims.

Claims

1. A solar-powered bank protection forest ecological monitoring and maintenance station, comprising: Solar energy module, ecological monitoring module and maintenance module; characterized in that the solar energy module uses solar panels to convert solar energy into electrical energy to provide power supply for the ecological monitoring module and the maintenance module; The ecological monitoring module includes a sensor system for real-time monitoring of the ecological environment parameters of the bank protection forest; and also includes an image acquisition system for real-time recording and recording the state changes of the bank protection forest; the ecological monitoring module fits the ecological environment parameters with the state changes to obtain the real-time state of the bank protection forest, and then compares the real-time state with the ideal state to obtain the state difference; The maintenance module receives the state difference sent from the ecological environment parameter, and uses the automatic irrigation system, temperature system and lighting system to maintain the bank protection forest until the state difference is eliminated.

2. The integrated solar-powered bank protection forest ecological monitoring and maintenance station according to claim 1 is characterized by: The sensor system includes a temperature sensor, a humidity sensor, a light sensor and a soil moisture sensor. The ecological monitoring module adopts a hybrid data fusion method to fuse the data monitored by the sensor system, and extracts features from the fused data through a Bayesian inference algorithm to obtain the ecological environment parameters.

3. The integrated solar-powered bank protection forest ecological monitoring and maintenance station according to claim 2 is characterized by: The process of obtaining the ecological environment parameters includes the following steps: S1, obtain the raw data of temperature, humidity, light, and soil moisture from the sensor system, and clean the raw data to remove noise and outliers; S2. Setting a unified timestamp in the sensor system for time synchronization between sensors in the sensor system; S3, normalize the data from different sensors and convert the data to a unified scale; S4. Using the Bayesian inference algorithm, extracting data features based on the probability principle, processing information with uncertainty, and obtaining the ecological environment parameters; wherein the formula expression of the Bayesian inference algorithm is: Where P(H|E) represents the posterior probability that the hypothesis H is true given the observed data E, P(E|H) represents the likelihood probability that the observed data E appears given the hypothesis H is true, P(H) represents the prior probability that the hypothesis H is true, and P(E) represents the marginal probability that the observed data E appears; The fixed observation data E refers to the actual measurement value collected by the sensor system. Therefore, E is a set of multiple sensor measurement values, specifically: E = {E T ,E H ,E L ,E S }; where E T E is the temperature value measured by the temperature sensor; H The humidity value measured by the humidity sensor; E L The light intensity value measured by the light sensor; E S The soil moisture content value measured by the soil moisture sensor.

4. The integrated solar-powered bank protection forest ecological monitoring and maintenance station according to claim 1 is characterized by: The real-time state fitting process includes the following steps: A1. Extract key features that can reflect the state of the bank protection forest from ecological environment parameters; extract image features that can reflect the changes in the state of the bank protection forest from the image acquisition system; A2. Use machine learning methods to construct a single linear fitting model, use the key features and image features in step A1 as data input, and train the single linear fitting model. During the training process, the single linear fitting model learns the relationship between the ecological environment parameters and the state changes of the bank protection forest, and obtains the change law; A3. The single linear fitting model outputs the real-time status of the bank protection forest according to the input characteristics and change rules.

5. The integrated solar-powered bank protection forest ecological monitoring and maintenance station according to claim 4 is characterized by: The formula expression of the single linear fitting model is: Z=β0+β1x1y1+β2x2y2+…+β n x n y n +e Where Z is the real-time status of the bank protection forest; x1, x2...x n are the extracted key features; y1, y2...y n are the extracted image features; β0, β1, β2...β n are the parameters of the single linear fitting model, where β0+β1+β2+…+β n =1; ε is the error term, which represents the difference between the model predicted value and the actual value.

6. The integrated solar-powered bank protection forest ecological monitoring and maintenance station according to claim 1 is characterized by: The ideal state is obtained based on the actual management experience and historical data of the bank protection forest, taking into account the performance of the bank protection forest in different seasons and different climatic conditions and the impact of human activities on it.

7. The integrated solar-powered bank protection forest ecological monitoring and maintenance station according to claim 1 is characterized by: The maintenance module also includes an intelligent decision-making system, which automatically formulates a maintenance strategy according to the size and nature of the state difference and controls the operation of the automatic irrigation system, the temperature system and the lighting system.

8. The integrated solar-powered bank protection forest ecological monitoring and maintenance station according to claim 1 is characterized by: It also includes a remote communication module for remotely transmitting the real-time status obtained by the ecological monitoring module, the working status of the maintenance module and the power supply status of the solar module to a monitoring center or a mobile terminal. Users can understand the ecological status and maintenance status of the bank protection forest in real time and conduct remote monitoring and management.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the implementation steps of the bank protection forest ecological monitoring and maintenance station described in any one of claims 1 to 8 are realized.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the implementation steps of the bank protection forest ecological monitoring and maintenance station described in any one of claims 1 to 8 are realized.

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