A Collaborative Management and Control System for Rural Environmental Monitoring and Assessment

Abstract

This invention relates to the field of environmental management, and more particularly to a collaborative management system for rural environmental monitoring and assessment. The system comprises several equally spaced planting holes. For each planting hole, the system includes: a camera module for photographing the surface vegetation and generating corresponding vegetation information; a soil-locking module for intercepting and retaining lost soil within a soil-locking net; a data collection module for filtering the lost soil, forming corresponding filtered soil, and acquiring loss information; a learning module for fitting the vegetation and loss information, generating corresponding fitted data, preprocessing it, and selecting several indicator features from the preprocessed data for learning; and a collaborative module for receiving the learning results from the learning module and adjusting the planting hole accordingly. This invention prevents water loss from the soil, acts as a water storage system, increases vegetation survival rate, improves the accuracy of environmental assessment and management, and plays a significant role in improving the rural environment.

Description

Technical Field

[0001] This invention relates to the field of environmental management, and in particular to a collaborative management system for rural environmental monitoring and environmental assessment. Background Technology

[0002] Soil erosion leads to the loss of soil fertility and thinning of the soil layer, which in turn affects land productivity and the stability of the ecosystem. Preventing soil erosion in rural areas is of profound and important significance. It is not only related to the health and stability of the rural ecological environment, but also directly affects the sustainability of agricultural production, the development of the rural economy, and the overall well-being of society.

[0003] Chinese Patent Application Publication No. CN116332365A discloses an ecological water environment management system for sandy riverbeds in mountainous areas. The system includes an ecological green island in the river's central area, an ecological buffer zone near the riverbank, an ecological slope protection near the main channel, and an ecological interception ditch along the water flow direction. The green space in the ecological buffer zone is planted sequentially from the water's edge to the riverbank with dwarf loosestrife, yellow iris, pampas grass, and creeping juniper. Two sediment retention zones are set in the middle of the dwarf loosestrife, sequentially designated as a volcanic rock sediment retention zone and a zeolite sediment retention zone from the water's edge. This water environment management system effectively resists river erosion and soil loss caused by large longitudinal slopes; it can coordinate water resources, improve water quality, and reduce non-point source pollution; the system is an effective measure to reduce nitrogen and phosphorus loads and improve the rural living environment.

[0004] Chinese Patent Application Publication No. CN113030969A discloses a method and system for monitoring deformation and soil erosion in open-pit mine spoil heaps. The method includes: acquiring multi-temporal synthetic aperture radar (SAR) image data of the spoil heap to obtain the total settlement distribution; dividing the spoil heap into several regions according to its stockpiling height using a high-precision digital elevation model; statistically analyzing the settlement distribution of each stockpiling height region based on the total settlement distribution and the divided regions, and obtaining the minimum value within each region to determine the consolidation settlement amount; establishing a regression relationship between the stockpiling height and consolidation settlement amount; inverting the consolidation settlement distribution of the spoil heap based on the regression relationship; and obtaining the surface erosion amount distribution caused by soil erosion based on the total settlement distribution and consolidation settlement distribution. The scheme in this application can monitor and understand the non-uniform deformation and soil erosion development patterns of open-pit mine spoil heaps, accurately assessing their environmental impact, scope, and long-term environmental pollution risks.

[0005] However, the above methods have the following problems: they cannot monitor and intelligently control the environment in real time, which makes it impossible to effectively adjust individual planting areas when soil erosion is severe, thus reducing the efficiency of collaboration. Summary of the Invention

[0006] To address this issue, the present invention provides a collaborative management and control system for rural environmental monitoring and assessment, which overcomes the problem in existing technologies that cannot monitor and intelligently regulate the environment in real time, resulting in the inability to effectively adjust individual planting areas when soil erosion is severe, thus leading to reduced collaborative efficiency.

[0007] To achieve the above objectives, this invention provides a collaborative management system for rural environmental monitoring and assessment. The system includes several planting holes spaced at equal intervals. For a single planting hole, the system includes:

[0008] The camera module is used to photograph the surface vegetation of the planting hole and generate corresponding vegetation information.

[0009] A soil-locking module is used to intercept lost soil and retain the lost soil in the soil-locking net;

[0010] The acquisition module, which is connected to the soil-locking module, is used to filter the lost soil to form corresponding filtered soil and to acquire the loss information of the filtered soil.

[0011] The learning module, which is connected to the shooting module and the acquisition module, is used to fit the vegetation information and the loss information, generate corresponding fitting data, preprocess the fitting data, generate corresponding preprocessed data, select several indicator features of the preprocessed data, and learn the preprocessed data based on the indicator features.

[0012] A collaborative module, connected to the learning module, is used to receive the learning results from the learning module and adjust the planting hole.

[0013] Wherein, the loss information refers to the water content in the lost soil;

[0014] The fitting data includes vegetation fitting data corresponding to the vegetation information and loss fitting data corresponding to the loss information.

[0015] The learning module includes a vegetation learning model and a churn learning model, which is trained and generated based on the preprocessed data.

[0016] Furthermore, the imaging module is equipped with a single camera positioned above a single planting hole to capture real-time images of the surface vegetation and convert the vegetation images into vegetation information for output.

[0017] The camera is equipped with a fixed time interval for polling whether the vegetation information is output at fixed intervals.

[0018] Furthermore, four downward-sloping protective holes are drilled at equal intervals on the side wall of the soil-locking module. These holes are filled with mud to fix the soil-locking mesh to a preset height. The soil-locking mesh intercepts the lost soil.

[0019] The preset height corresponds to the lowest position of the main stem of the vegetation root system.

[0020] Furthermore, the filter screen in the acquisition module filters the lost soil, and the filtered soil automatically falls off the filter screen to form corresponding filtered soil. The loss information in the filtered soil is monitored.

[0021] The filter screen is used to filter out large particulate impurities from the lost soil.

[0022] Furthermore, the learning module fits the vegetation information and the loss information, and generates several fitted data sets with a sampling rate of the standard sampling rate.

[0023] The fitting process involves segmenting the vegetation information and the loss information according to the standard sampling rate.

[0024] The standard sampling rate is the sampling rate that the vegetation learning model and the churn learning model can recognize, and for a single sampling, the corresponding standard sampling rate is the single sampling rate.

[0025] Furthermore, the preprocessor preprocesses the vegetation fitting data to generate corresponding vegetation data. The vegetation learning model selects several indicator features from the vegetation data, learns from the vegetation data using the vegetation learning model, and generates the loss threshold corresponding to the surface vegetation.

[0026] The vegetation data includes indicators such as water content, weather conditions, elevation, and / or vegetation type.

[0027] The loss threshold is the maximum value of water loss corresponding to the surface vegetation.

[0028] Furthermore, the preprocessor preprocesses the churn fitting data to generate corresponding churn data. The churn learning model selects several indicator features from the churn data, learns from the churn data using the churn learning model, and generates a churn collaboration graph.

[0029] The churn learning model learns from the churn data by converting the churn data into the churn collaboration graph, extracting several indicator features from the churn collaboration graph, and labeling the churn collaboration graph based on the indicator features.

[0030] The indicators of the lost data include weather conditions, monitoring time and / or terrain elevation.

[0031] Furthermore, the collaboration module receives the loss collaboration map, and the calculator calculates the real-time water loss content corresponding to each planting hole.

[0032] Furthermore, the water loss content is compared with the loss threshold. When the water loss content is less than the loss threshold, the collaborative module does not make any adjustments to the planting hole.

[0033] Furthermore, when the water loss exceeds the loss threshold, the collaborative module configures the elements required for vegetation growth into a nutrient solution and irrigates the planting hole.

[0034] Compared with existing technologies, this invention, by providing several equally spaced planting holes, includes the following components for each planting hole: a camera module for photographing the surface vegetation of the planting hole and generating corresponding vegetation information; a soil-locking module for intercepting lost soil and retaining it in a soil-locking net; a data collection module for filtering the lost soil, forming corresponding filtered soil and acquiring loss information; a learning module for fitting the vegetation and loss information, generating corresponding fitted data and preprocessing it, and selecting several indicator features from the preprocessed data for learning; and a coordination module for receiving the learning results from the learning module and adjusting the planting hole. This invention prevents water loss from the soil, plays a water storage role, increases the survival rate of vegetation, improves the accuracy of environmental assessment and management, and plays an important role in improving the rural environment.

[0035] Furthermore, by setting up a camera module to photograph surface vegetation, it is possible to obtain the loss threshold corresponding to specific vegetation, thereby improving the accuracy of environmental assessment and management.

[0036] Furthermore, by setting up a soil-locking net in the soil-locking module, lost soil can be intercepted, ensuring real-time collection of lost soil and improving the accuracy of moisture monitoring in lost soil.

[0037] Furthermore, by setting up a filter screen, large particulate impurities lost in the soil can be filtered out, avoiding errors caused by large particulate impurities to the learning results and improving the accuracy of the system's environmental detection.

[0038] Furthermore, by fitting and preprocessing the fitted data through the learning module, corresponding preprocessed data is obtained, which enables the rapid selection of effective features from the preprocessed data. This facilitates the learning of the vegetation learning model and the leaching learning model from the preprocessed data, further improving the system's efficiency in cooperating with the environment.

[0039] Furthermore, by obtaining the loss threshold through the vegetation learning model, corresponding adjustment measures can be taken for the corresponding planting holes, which can play a role in water storage, prevent water loss from the soil, increase the survival rate of vegetation, and further improve the rural environment. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the structure of a single planting hole in a collaborative management and control system for rural environmental monitoring and environmental assessment according to the present invention;

[0041] Figure 2 This is a schematic diagram of the shooting area of ​​the shooting module of the present invention;

[0042] Figure 3 This is a schematic diagram of the soil-locking module of the present invention;

[0043] Figure 4 This is a schematic diagram of the fixed position of the soil-locking net of the present invention;

[0044] The components are: 1. Root system and main trunk; 2. Planting hole; 3. Soil-locking net; 4. Shooting area; 5. Other vegetation; 6. Protective hole. Detailed Implementation

[0045] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0046] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0048] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0049] Please see Figure 1 As shown, it is a structural schematic diagram of a single planting hole in a collaborative management and control system for rural environmental monitoring and assessment according to the present invention, comprising:

[0050] The camera module is used to photograph the surface vegetation of the planting hole and generate corresponding vegetation information;

[0051] Soil-locking modules are used to intercept lost soil and retain it within the soil-locking mesh.

[0052] The data acquisition module, which is connected to the soil locking module, is used to filter the lost soil, form corresponding filtered soil, and acquire information on the loss of the filtered soil.

[0053] The learning module, which is connected to the shooting module and the acquisition module, is used to fit vegetation information and loss information, generate corresponding fitted data, preprocess the fitted data, generate corresponding preprocessed data, select several indicator features of the preprocessed data, and learn the preprocessed data based on the indicator features.

[0054] The collaboration module, which is connected to the learning module, is used to receive the learning results from the learning module and adjust the planting holes;

[0055] Among them, the loss information refers to the water content in the lost soil;

[0056] The fitting data includes vegetation fitting data corresponding to vegetation information and loss fitting data corresponding to loss information;

[0057] The learning module includes a vegetation learning model and a churn learning model. The churn learning model is trained and generated based on preprocessed data.

[0058] By using planting holes spaced at equal intervals, each planting hole includes: a camera module for photographing the surface vegetation and generating corresponding vegetation information; a soil-locking module for intercepting lost soil and retaining it in a soil-locking net; a data collection module for filtering the lost soil, forming corresponding filtered soil and acquiring loss information; a learning module for fitting the vegetation and loss information, generating corresponding fitted data and preprocessing it, and selecting several indicator features from the preprocessed data for learning; and a coordination module for receiving the learning results from the learning module and adjusting the planting hole. This invention prevents water loss from the soil, plays a water storage role, increases the survival rate of vegetation, improves the accuracy of environmental assessment and management, and plays an important role in improving the rural environment.

[0059] Please see Figure 2The diagram shows the shooting area of ​​the shooting module of this invention. A single camera is installed in the shooting area 4, positioned above the root trunk 1. The camera captures real-time images of the surface vegetation, filters out other vegetation 5 not belonging to the root trunk 1, and generates a vegetation image. This vegetation image is then converted into vegetation information for output.

[0060] The camera is equipped with a fixed time interval to poll whether vegetation information is output at fixed intervals.

[0061] By setting up a camera module to photograph surface vegetation, the loss threshold of specific vegetation can be obtained, improving the accuracy of environmental assessment and management.

[0062] Please see Figure 3 As shown, it is a structural schematic diagram of the soil-locking module of the present invention. In this invention, four downwardly inclined protective holes 6 are drilled at equal intervals on the side wall of the planting hole 2. The protective holes 6 are filled with mud and the soil-locking net 3 is fixed at a preset height. The soil-locking net 3 intercepts the lost soil.

[0063] Please see Figure 4 As shown, it is a schematic diagram of the fixed position of the soil-locking net of the present invention. The root trunk 1 is placed in the planting hole 2, and the preset height of the soil-locking net 3 should not be higher than the lowest position of the root trunk 1.

[0064] By setting up a soil-locking net in the soil-locking module, lost soil can be intercepted, ensuring real-time collection of lost soil and improving the accuracy of moisture monitoring in lost soil.

[0065] Specifically, the filter screen in the data acquisition module filters the lost soil. The filtered soil automatically falls off the filter screen, forming corresponding filtered soil. The system monitors the loss information in the filtered soil.

[0066] Among them, the filter screen is used to filter out large particles of impurities lost from the soil.

[0067] By setting up a filter screen, large particles of impurities lost from the soil can be filtered out, avoiding errors caused by large particles to the learning results and improving the accuracy of the system's environmental detection.

[0068] Specifically, the learning module fits vegetation and loss information and generates several fitted data sets with a sampling rate of the standard sampling rate.

[0069] Among them, fitting involves segmenting vegetation information and loss information according to a standard sampling rate;

[0070] The standard sampling rate is the sampling rate that the vegetation learning model and the churn learning model can recognize, and for a single sampling, the corresponding standard sampling rate is the single sampling rate.

[0071] In practice, the preferred standard sampling rate is set to 10 samples per second, which yields the most accurate learning results from the learning module.

[0072] Specifically, the preprocessor preprocesses the vegetation fitting data to generate corresponding vegetation data. The vegetation learning model selects several indicator features from the vegetation data, learns from the vegetation data using the vegetation learning model, and generates the loss threshold corresponding to the surface vegetation.

[0073] Among them, the indicators of vegetation data include water content, weather conditions, terrain elevation and / or vegetation species.

[0074] The loss threshold is the maximum water loss content corresponding to surface vegetation.

[0075] Example 1:

[0076] For a certain planting hole, the shooting module takes pictures of the surface vegetation and converts it into vegetation information. The vegetation learning model selects the following index features: "water content: 15%, weather conditions: sunny, terrain: 0m, vegetation type: poplar". The loss threshold of the generated surface vegetation is 5%.

[0077] By obtaining the loss threshold through a vegetation learning model, corresponding adjustments can be made to the planting holes, which can play a role in water storage, prevent water loss from the soil, increase the survival rate of vegetation, and further improve the rural environment.

[0078] Specifically, the preprocessor preprocesses the churn fitting data to generate corresponding churn data. The churn learning model selects several indicator features from the churn data, learns from the churn data using the churn learning model, and generates a churn collaboration graph.

[0079] Among them, the churn learning model learns from churn data by converting the churn data into a churn collaboration graph, extracting several indicator features from the churn collaboration graph, and labeling the churn collaboration graph based on these indicator features.

[0080] The key characteristics of lost data include weather conditions, monitoring time and / or elevation.

[0081] The learning module fits and preprocesses the fitted data to obtain corresponding preprocessed data, which enables the rapid selection of effective features from the preprocessed data. This facilitates the learning of the vegetation learning model and the leaching learning model from the preprocessed data, further improving the system's efficiency in cooperating with the environment.

[0082] Specifically, the collaboration module receives the water loss collaboration map, and the calculator calculates the real-time water loss content of each planting hole.

[0083] Specifically, the water loss content is compared with the loss threshold. When the water loss content is less than the loss threshold, the collaborative module does not make any adjustments to the planting hole.

[0084] Example 2:

[0085] If the loss threshold is set to 5%, and the calculator calculates that the real-time water loss content corresponding to the loss in the collaborative graph of planting hole A is 4%, which is less than the loss threshold of 5%, then the collaborative module will not make any adjustments to planting hole A.

[0086] Specifically, when the water loss exceeds the loss threshold, the collaborative module will configure the elements needed for vegetation growth into a nutrient solution and irrigate the planting holes.

[0087] Example 3:

[0088] If the loss threshold is set to 5%, and the real-time water loss content corresponding to the loss collaboration graph of planting hole B is 10%, which is greater than the loss threshold of 5%, then the collaboration module will prepare a nutrient solution of nitrogen, phosphorus and potassium required for vegetation growth in a ratio of 3:1:1 and irrigate the planting hole.

[0089] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0090] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A collaborative management and control system for rural environmental monitoring and assessment, characterized in that, The system has several planting holes arranged at equal intervals. For a single planting hole, it includes: The camera module is used to photograph the surface vegetation of the planting hole and generate corresponding vegetation information. A soil-locking module is used to intercept lost soil and retain the lost soil in the soil-locking net; The acquisition module, which is connected to the soil-locking module, is used to filter the lost soil to form corresponding filtered soil and to acquire the loss information of the filtered soil. The learning module, which is connected to the shooting module and the acquisition module, is used to fit the vegetation information and the loss information, generate corresponding fitting data, preprocess the fitting data, generate corresponding preprocessed data, select several indicator features of the preprocessed data, and learn the preprocessed data based on the indicator features. A collaborative module, connected to the learning module, is used to receive the learning results from the learning module and adjust the planting hole. Wherein, the loss information refers to the water content in the lost soil; The fitting data includes vegetation fitting data corresponding to the vegetation information and loss fitting data corresponding to the loss information. The learning module includes a vegetation learning model and a leaching learning model, and the leaching learning model is trained and generated based on the preprocessed data. Four downward-sloping protective holes are drilled at equal intervals on the side wall of the soil-locking module. These holes are filled with mud, and the soil-locking mesh is fixed at a preset height. The soil-locking mesh intercepts the lost soil. The preset height corresponds to the lowest position of the main stem of the vegetation root system; The filter screen in the acquisition module filters the lost soil. The filtered soil automatically falls off the filter screen to form corresponding filtered soil. The loss information in the filtered soil is monitored. The filter screen is used to filter out large particulate impurities in the lost soil. The learning module fits the vegetation information and the loss information, and generates several fitted data sets with a sampling rate of the standard sampling rate. The fitting process involves segmenting the vegetation information and the loss information according to the standard sampling rate. The standard sampling rate is the sampling rate that the vegetation learning model and the churn learning model can recognize, and for a single sampling, the corresponding standard sampling rate is the single sampling rate. The preprocessor preprocesses the vegetation fitting data to generate corresponding vegetation data. The vegetation learning model selects several indicator features from the vegetation data, learns from the vegetation data using the vegetation learning model, and generates the loss threshold corresponding to the surface vegetation. The vegetation data includes indicators such as water content, weather conditions, elevation, and / or vegetation type. The loss threshold is the maximum value of water loss corresponding to the surface vegetation.

2. The rural environmental monitoring and environmental assessment collaborative management system according to claim 1, characterized in that, The imaging module is equipped with a single camera, which is positioned above a single planting hole to capture real-time images of the surface vegetation and convert the vegetation images into vegetation information for output. The camera is equipped with a fixed time interval for polling whether the vegetation information is output at fixed intervals.

3. The rural environmental monitoring and environmental assessment collaborative management system according to claim 2, characterized in that, The preprocessor preprocesses the churn fitting data to generate corresponding churn data. The churn learning model selects several indicator features from the churn data, learns from the churn data using the churn learning model, and generates a churn collaboration graph. The churn learning model learns from the churn data by converting the churn data into the churn collaboration graph, extracting several indicator features from the churn collaboration graph, and labeling the churn collaboration graph based on the indicator features. The indicators of the lost data include weather conditions, monitoring time and / or terrain elevation.

4. The rural environmental monitoring and environmental assessment collaborative management system according to claim 3, characterized in that, The collaboration module receives the water loss collaboration map, and the calculator calculates the real-time water loss content of each planting hole.

5. The rural environmental monitoring and environmental assessment collaborative management system according to claim 4, characterized in that, The water loss content is compared with the loss threshold. When the water loss content is less than the loss threshold, the collaborative module does not make any adjustments to the planting hole.

6. The rural environmental monitoring and environmental assessment collaborative management system according to claim 5, characterized in that, When the water loss exceeds the loss threshold, the collaborative module will configure the elements required for vegetation growth into a nutrient solution and irrigate the planting hole.

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