Construction system of ecological environment database

Abstract

The present application discloses a system for constructing an ecological environment database, including: a water quality data acquisition module, a water quality data processing module, a water quality data feature extraction module, a water quality data pollution detection and analysis model training module and a model application integration module. It adopts technical means such as a water quality data acquisition module, so as to effectively solve the technical problems in the prior art, and further realizes that when collecting water quality ecological environment data, the water quality data is collected by sensor equipment, and the water quality data is analyzed according to the model, so as to detect and analyze the pollutants in the water, detect the pollutants in the water, and thus achieve the effect of improving the management of water.

Description

Technical Field

[0001] The present application relates to the technical field of ecological environment data collection, and more specifically, to a system for constructing an ecological environment database. Background Art

[0002] Ecological and environmental data usually include various data collected from natural and human-made systems. The following are some common types of ecological and environmental data.

[0003] 1. Climate data: including temperature, precipitation, wind speed, humidity, etc., used to analyze and predict the impact of climate change on the ecosystem.

[0004] 2. Water quality data: including indicators such as dissolved oxygen, ammonia nitrogen, chemical oxygen demand (COD), total nitrogen, total phosphorus, pH value, etc. in water, which are used to assess the degree of eutrophication of water bodies, water pollution, etc.

[0005] 3. Soil data: including soil organic matter content, soil texture, soil pH and other indicators in the soil, which are used to evaluate the quality and suitability of the soil.

[0006] 4. Vegetation data: including plant species composition, vegetation coverage, growth status and other indicators, which are used to assess the health status of vegetation and habitat quality.

[0007] The document with the prior art publication number CN110208136A provides a system for constructing an ecological environment database. The device obtains soil moisture data and soil sampling penetration pressure data in different regions through a soil moisture acquisition module and a soil sampling penetration pressure acquisition module. The second analysis module analyzes the possibility of debris flow disasters based on the soil moisture data and soil sampling penetration pressure data of different sampling areas, completes the construction of the ecological environment database, and can play a good role in preventing the occurrence of debris flow disasters.

[0008] Although the above-mentioned prior art solution can achieve the relevant beneficial effects through the structure of the prior art, it still has the following defects: when the device is in use, it realizes the collection of soil data, but it is not convenient for collecting ecological data on the water quality of the river sewage outlet, resulting in its poor practicality.

[0009] In view of this, we propose a system for constructing an ecological environment database. Summary of the invention

[0010] 1. Technical problems to be solved.

[0011] The purpose of this application is to provide a system for constructing an ecological environment database, solve the technical problems raised in the above-mentioned background technology, and achieve the technical effect of collecting the water quality ecological environment of the river sewage outlet.

[0012] 2. Technical solution.

[0013] The embodiment of the present application provides a system for constructing an ecological environment database, including: a water quality data acquisition module, a water quality data processing module, a water quality data feature extraction module, a water quality data pollution detection and analysis model training module and a model application integration module.

[0014] Water quality data collection module: The water quality data collection module collects ecological and environmental parameters such as water quality, water level, water flow, etc. of the river sewage outlet in real time by using sensors and other equipment.

[0015] Water quality data processing module: The water quality data processing module pre-processes the collected raw data to make the data have certain reliability and availability.

[0016] Water quality data feature extraction module: The water quality data feature extraction module extracts data features about water pollution from the processed data.

[0017] Water quality data pollution detection and analysis model training module: The water quality data pollution detection and analysis model training module selects a suitable machine learning model based on the collected data and the required analysis objectives, so that it can predict or classify the ecological environment conditions of the river discharge outlet based on the input characteristics.

[0018] Model application integration module: The model application integration module will use the trained machine learning model to analyze the collected data, process the water quality ecological environment data at the river discharge outlet, and integrate it with the ecological environment database.

[0019] As an optional solution of the technical solution of the present application document, the water quality data acquisition module includes collecting various ecological and environmental parameters related to water quality, through a pH sensor: used to measure the acidity and alkalinity of the water body, and to evaluate the water quality at the sewage outlet by measuring the hydrogen ion concentration (pH value) in the water; through a turbidity sensor: to measure the concentration of suspended particles and suspended matter in the water body, reflecting the clarity of the water body at the sewage outlet; through a water body colorimetry sensor: used to measure the colorimetry of the water body, reflecting the content data of dyes, soluble particles and suspended matter in the water, etc.

[0020] As an optional solution of the technical solution of the present application document, the water quality data processing module pre-processes the collected raw data, including data cleaning, data smoothing and data calibration. The data cleaning: fills in missing values ​​through interpolation methods to detect and process outliers, missing values ​​and noise in the data and applies filter technology to filter noise.

[0021] The data smoothing is performed by linear interpolation to fill in the missing data due to equipment failure or other reasons. The interpolation method can be based on known data points around the sensor, or can be interpolated using the trend of the time series.

[0022] The data calibration: utilizes a calibration method based on standard reference data to correct the measurement deviation and drift of the sensor device for automatic calibration, thereby improving the quality and accuracy of the data for subsequent data analysis and application.

[0023] As an optional solution of the technical solution of this application document, the water quality data feature extraction module extracts data features about water pollution from the processed data, including statistical features, correlation features and spatial features. The statistical features provide basic statistical information about water quality parameters, such as average water quality value, degree of data dispersion, etc., through the statistical characteristics of the average value.

[0024] The correlation features are used to discover the correlation between water quality parameters. By calculating the correlation coefficient or correlation matrix between the water quality parameters, the relationship between different parameters can be understood, and possible correlations or influencing factors can be detected.

[0025] The spatial characteristics: by calculating the spatial variation trend, spatial correlation and other characteristics of water quality parameters, it is possible to understand the pollution degree or change of water bodies in different regions.

[0026] As an optional solution to the technical solution of the present application document, the water quality data pollution detection and analysis model training module selects the water quality data pollution detection and analysis model of the regression model, divides the collected water quality data into a training set and a test set, uses the training set to train the selected machine learning model, and iteratively optimizes the model parameters to achieve a better fitting effect on the training set, and uses the test set to evaluate the performance of the model.

[0027] As an optional solution to the technical solution of this application document, the model application integration module will use the trained machine learning model to analyze the collected data, process the water quality ecological environment data at the river sewage outlet, and integrate it with the ecological environment database. When it is monitored that the water quality of the sewage outlet is polluted, the location of the sewage outlet is marked, and the source of the sewage is found through the sewage pipe of the sewage outlet, and the sewage source is cut off to ensure the water quality ecological environment.

[0028] 3. Beneficial effects.

[0029] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages.

[0030] 1. Since this application adopts technical means such as water quality data acquisition module, it effectively solves the technical problems in the prior art, and then realizes the use of sensor equipment to collect water quality data when collecting water quality ecological environment data, and analyzes the water quality data according to the model, so as to detect and analyze the pollutants in the water, detect the pollutants in the water, and thus achieve the effect of improving water management.

[0031] 2. This application enables the water quality regulating and collecting mechanism to contact the water to be tested by setting up a swing frame. After the test is completed, the water quality regulating and collecting mechanism can be stored to collect water quality data in the river, thereby improving the practicality of the device.

[0032] 3. This application provides a cleaning mechanism, which can clean the sensor device after use to remove residual water stains, thereby ensuring the accuracy of the next water quality detection data and further improving the practicality of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic diagram of the water quality collection and analysis process of the overall structure of the ecological environment database construction system disclosed in a preferred embodiment of the present application.

[0034] Figure 2 This is a schematic diagram of the overall structure of water quality detection of a system for constructing an ecological environment database disclosed in a preferred embodiment of the present application.

[0035] Figure 3 This is a schematic cross-sectional view of the overall structure of a system for constructing an ecological environment database disclosed in a preferred embodiment of the present application.

[0036] Figure 4 It is a partial structural cross-sectional schematic diagram of a system for constructing an ecological environment database disclosed in a preferred embodiment of the present application.

[0037] Figure 5 This is a schematic cross-sectional view of the structure of the rotating frame and water quality regulation and collection mechanism of the ecological environment database construction system disclosed in a preferred embodiment of the present application.

[0038] Figure 6 This is a schematic cross-sectional view of the cleaning mechanism structure of the ecological environment database construction system disclosed in a preferred embodiment of the present application.

[0039] Explanation of the numbers in the figure: 1. Remote control hull; 2. Image transmission equipment; 3. Swing frame; 4. Water quality adjustment and collection mechanism; 5. Cleaning mechanism; 201. Fixed frame; 202. Monitoring camera; 301. Motor A; 401. Connecting rod group; 402. Adjusting wheel; 403. Rack; 404. Slider; 405. Tension spring; 406. Retracting rod; 407. Mounting frame; 501. Motor B; 502. Rotating frame; 503. Reciprocating screw; 504. Transmission wheel; 505. Sponge; 101. Concave block; 102. Annular rack. DETAILED DESCRIPTION

[0040] The present application is further described in detail below in conjunction with the accompanying drawings.

[0041] Reference Figure 1 The construction system of the ecological environment database includes: water quality data acquisition module, water quality data processing module, water quality data feature extraction module, water quality data pollution detection and analysis model training module and model application integration module.

[0042] Water quality data collection module: The water quality data collection module collects ecological and environmental parameters such as water quality, water level, water flow, etc. at river discharge outlets in real time by using sensors and other equipment.

[0043] Water quality data processing module: The water quality data processing module pre-processes the collected raw data to make the data have a certain degree of reliability and availability.

[0044] Water quality data feature extraction module: The water quality data feature extraction module extracts data features about water pollution from the processed data.

[0045] Water quality data pollution detection and analysis model training module: The water quality data pollution detection and analysis model training module selects a suitable machine learning model based on the collected data and the objectives to be analyzed, so that it can predict or classify the ecological environment of the river discharge outlet based on the input characteristics.

[0046] Model application integration module: The model application integration module will use the trained machine learning model to analyze the collected data, process the water quality ecological environment data at the river discharge outlet, and integrate it with the ecological environment database.

[0047] The water quality data acquisition module includes collecting various ecological and environmental parameters related to water quality, through pH sensors: used to measure the acidity and alkalinity of water bodies, and to evaluate the water quality at the sewage outlet by measuring the pH value of the hydrogen ion concentration in the water; through turbidity sensors: measuring the concentration of suspended particles and suspended matter in the water body, reflecting the clarity of the water body at the sewage outlet; through water color sensors: used to measure the color of the water body, reflecting the content data of dyes, soluble particles and suspended matter in the water, etc.

[0048] The water quality data processing module pre-processes the collected raw data, including data cleaning, data smoothing and data calibration. Data cleaning: fill in missing values ​​through interpolation methods to detect and process outliers, missing values ​​and noise in the data and apply filter technology to filter noise.

[0049] Data smoothing: Linear interpolation is used to fill in data gaps caused by equipment failure or other reasons. This interpolation method can be based on known data points around the sensor or can use the trend of the time series for interpolation.

[0050] Data calibration: Calibration methods based on standard reference data are used to correct measurement deviations and drifts of sensor equipment for automatic calibration, thereby improving data quality and accuracy for subsequent data analysis and application.

[0051] The water quality data feature extraction module extracts data features about water pollution from the processed data, including statistical features, correlation features and spatial features. Statistical features: Through the statistical characteristics of the average value, it provides basic statistical information about water quality parameters, such as the average water quality value, the degree of data dispersion, etc.

[0052] Correlation features: used to discover the correlation between water quality parameters. By calculating the correlation coefficient or correlation matrix between water quality parameters, it is possible to understand the relationship between different parameters and detect possible correlations or influencing factors.

[0053] Spatial characteristics: By calculating the spatial variation trend, spatial correlation and other characteristics of water quality parameters, we can understand the degree of pollution or changes in water bodies in different regions.

[0054] The water quality data pollution detection and analysis model training module selects the water quality data pollution detection and analysis model of the regression model, divides the collected water quality data into a training set and a test set, uses the training set to train the selected machine learning model, and iteratively optimizes the model parameters to achieve a better fitting effect on the training set, and uses the test set to evaluate the performance of the model.

[0055] The model application integration module will use the trained machine learning model to analyze the collected data, process the water quality and ecological environment data at the river sewage outlet, and integrate it with the ecological environment database. When it is monitored that the water quality at the sewage outlet is polluted, the location of the sewage outlet will be marked, and the source of the sewage will be found through the sewage pipe of the sewage outlet, and the sewage source will be cut off to ensure the water quality ecological environment.

[0056] Reference Figure 2 and Figure 3The construction system of the ecological environment database is realized through the ecological environment data collection device, including a remote-controlled hull 1, an image transmission device 2, a swing frame 3, a water quality adjustment and collection mechanism 4 and a cleaning mechanism 5.

[0057] An image transmission device 2 is fixedly connected to the remote-controlled hull 1 , a swing frame 3 is rotatably connected to the remote-controlled hull 1 , a water quality adjustment and collection mechanism 4 is rotatably connected to the swing frame 3 , and a cleaning mechanism 5 is rotatably connected inside the remote-controlled hull 1 .

[0058] Reference Figure 4 The image transmission device 2 includes a fixing frame 201 , and monitoring cameras 202 are installed at both ends of the fixing frame 201 .

[0059] Reference Figure 5 One end of the swing frame 3 is connected and fixedly provided with a motor A301.

[0060] The water quality regulating and collecting mechanism 4 comprises a connecting rod group 401, one end of which is rotatably connected to the swing frame 3, one end of the connecting rod group 401 is connected and fixedly provided with an adjusting wheel 402, one side of the adjusting wheel 402 is meshingly driven with a rack 403, the rack 403 is slidably matched with the swing frame 3, a concave block 101 is connected and fixedly provided on the remote control hull 1, and the bottom end of the rack 403 is slidably contacted with the concave block 101.

[0061] A slider 404 is hingedly provided at the other end of the connecting rod group 401, and the slider 404 is slidably matched with the swing frame 3. One end of the slider 404 is fixedly connected to the swing frame 3 through a tension spring 405. A retracting rod 406 is hingedly provided at the other end of the slider 404, and a mounting frame 407 is hingedly provided at the other end of the retracting rod 406. One end of the mounting frame 407 is slidably matched with the swing frame 3, and a plurality of sensor devices are installed at the bottom of the mounting frame 407.

[0062] Reference Figure 6 The cleaning mechanism 5 includes a motor B501, the output end of the motor B501 is connected and fixedly provided with a rotating frame 502, the inner wall of one end of the rotating frame 502 is rotatably connected with a reciprocating screw rod 503, one end of the outer wall of the reciprocating screw rod 503 is connected and fixedly provided with a transmission wheel 504, the outer wall of the reciprocating screw rod 503 is threadedly connected with a sponge 505, and an annular rack 102 is connected and fixedly provided inside the remote control hull 1, and the annular rack 102 is meshed with the transmission wheel 504 for transmission.

[0063] The implementation principle of the construction system of the ecological environment database in the embodiment of the present application is: when it is necessary to collect water quality ecological data of the river discharge outlet, first control the remote control hull 1 to move in the river, and then remotely view the environment on both sides of the river through the monitoring camera 202.

[0064] Then when it moves to the sewage outlet, the motor A301 will drive the swing frame 3 to rotate, so that the water quality adjustment and collection mechanism 4 will move to one side, and then under the action of the concave block 101, the rack 403 will move up, so that the connecting rod group 401 drives the slider 404 to move, and then under the action of the retracting rod 406, the mounting frame 407 will move down, so that the sensor equipment can be inserted into the water to collect water quality data.

[0065] At this time, the water quality data acquisition module collects various ecological and environmental parameters about water quality, including the pH sensor used to measure the acidity and alkalinity of the water body, and evaluates the water quality at the sewage outlet by measuring the hydrogen ion concentration and pH value in the water. The turbidity sensor measures the concentration of suspended particles and suspended matter in the water body to reflect the clarity of the water body at the sewage outlet; the water color sensor is used to measure the color of the water body to reflect the content data of dyes, soluble particles and suspended matter in the water.

[0066] Then, the water quality data processing module is used to pre-process the collected raw data, including data cleaning, data smoothing and data calibration. Data cleaning: missing values ​​are filled by interpolation methods to detect and process outliers, missing values ​​and noise in the data, and filter technology is applied to filter noise; data smoothing: linear interpolation is used to fill in data missing due to equipment failure or other reasons. This interpolation method can be based on known data points around the sensor, and can also use the trend of the time series for interpolation; data calibration: a calibration method based on standard reference data is used to correct the measurement deviation and drift of the sensor equipment for automatic calibration, thereby improving the quality and accuracy of the data for subsequent data analysis and application.

[0067] By utilizing the water quality data feature extraction module, data features about water pollution are extracted from the processed data, including statistical features, correlation features and spatial features. Statistical features: provide basic statistical information about water quality parameters, such as average water quality value, data dispersion, etc. through the statistical characteristics of the average value; Correlation features: used to discover the correlation between water quality parameters. By calculating the correlation coefficient or correlation matrix between water quality parameters, the relationship between different parameters can be understood, and possible correlations or influencing factors can be detected; Spatial features: by calculating the spatial change trend, spatial correlation and other features of water quality parameters, the pollution degree or changes of water bodies in different regions can be understood.

[0068] Then, according to the model application integration module, the trained machine learning model will be used to analyze the collected data, process the water quality and ecological environment data at the river sewage outlet, and integrate it with the ecological environment database. When the water quality of the sewage outlet is monitored to be polluted, the location of the sewage outlet will be marked, and the source of the sewage will be found through the sewage pipe of the sewage outlet, and the sewage source will be cut off to ensure the water quality ecological environment.

[0069] After the data collection is completed, the water quality adjustment and collection mechanism 4 is placed in the remote control hull 1. At this time, the cleaning liquid inside will soak the sensor equipment. At the same time, the motor B501 is used to drive the connected rotating frame 502 to rotate. Then, under the action of the annular rack 102, the reciprocating screw rod 503 connected to the transmission wheel 504 will rotate, thereby driving the sponge 505 to move up and down to wipe and clean the sensor equipment.

Claims

1. The construction system of the ecological environment database includes: water quality data acquisition module, water quality data processing module, water quality data feature extraction module, water quality data pollution detection and analysis model training module and model application integration module, which is characterized by: Water quality data acquisition module: The water quality data acquisition module collects the ecological environment parameters of the river discharge outlet in real time by using sensors. The ecological environment parameters include water quality, water level and water flow; Water quality data processing module: The water quality data processing module pre-processes the collected raw data to make the data reliable and available; Water quality data feature extraction module: The water quality data feature extraction module extracts data features about water pollution from the processed data; Water quality data pollution detection and analysis model training module: The water quality data pollution detection and analysis model training module selects a suitable machine learning model based on the collected data and the target of the analysis, so that it can predict or classify the ecological environment of the river discharge outlet according to the input characteristics; Model application integration module: The model application integration module will use the trained machine learning model to analyze the collected data, process the water quality ecological environment data at the river discharge outlet, and integrate it with the ecological environment database; The construction system of the ecological environment database is realized through the ecological environment data collection device, including a remote-controlled hull, an image transmission device, a swing frame, a water quality adjustment and collection mechanism, and a cleaning mechanism. An image transmission device is fixedly connected to the remote control hull, a swing frame is rotatably connected to the remote control hull, a water quality adjustment and collection mechanism is rotatably connected to the swing frame, and a cleaning mechanism is rotatably connected to the remote control hull; The image transmission device includes a fixed frame, and monitoring cameras are installed at both ends of the fixed frame; One end of the swing frame is connected and fixedly provided with a motor A; The water quality regulating and collecting mechanism comprises a connecting rod group, one end of the connecting rod group is rotatably connected to the swing frame, one end of the connecting rod group is connected and fixedly provided with an adjusting wheel, one side of the adjusting wheel is meshingly provided with a rack, the rack is slidably matched with the swing frame, a concave block is connected and fixedly provided on the remote control hull, and the bottom end of the rack is slidably contacted with the concave block; A slider is hingedly provided at the other end of the connecting rod group, and the slider is slidably matched with the swing frame. One end of the slider is connected and fixed to the swing frame through a tension spring. A retracting rod is hingedly provided at the other end of the slider, and a mounting frame is hingedly provided at the other end of the retracting rod. One end of the mounting frame is slidably matched with the swing frame, and a plurality of sensor devices are installed at the bottom of the mounting frame. The cleaning mechanism includes a motor B, the output end of the motor B is connected and fixedly provided with a rotating frame, the inner wall of one end of the rotating frame is rotatably connected with a reciprocating screw, one end of the outer wall of the reciprocating screw is connected and fixedly provided with a transmission wheel, the outer wall of the reciprocating screw is threadedly connected with a sponge, and an annular rack is connected and fixedly provided inside the remote control hull, and the annular rack is meshed with the transmission wheel for transmission.

2. The ecological environment database construction system according to claim 1, characterized in that: The water quality data acquisition module includes collecting water quality ecological environment parameters, through pH sensors: used to measure the acidity and alkalinity of water bodies, and evaluating the water quality at the sewage outlet by measuring the hydrogen ion concentration in the water. Through turbidity sensors: measure the concentration of suspended particles and suspended matter in the water body to reflect the clarity of the water body at the sewage outlet; through water color sensors: used to measure the color of the water body to reflect the content data of dyes, soluble particles and suspended matter in the water.

3. The ecological environment database construction system according to claim 1, characterized in that: The water quality data processing module pre-processes the collected raw data, including data cleaning, data smoothing and data calibration. The data cleaning includes filling missing values ​​by interpolation method to detect and process abnormal values, missing values ​​and noise in the data and applying filter technology to filter noise; Data smoothing: Linear interpolation is used to fill in data gaps caused by equipment failure or other reasons. This interpolation method can be based on known data points around the sensor or can be interpolated using the trend of the time series. The data calibration: utilizes a calibration method based on standard reference data to correct the measurement deviation and drift of the sensor device for automatic calibration, thereby improving the quality and accuracy of the data for subsequent data analysis and application.

4. The ecological environment database construction system according to claim 1, characterized in that: The water quality data feature extraction module extracts data features about water pollution from the processed data, including statistical features, correlation features and spatial features. The statistical features provide basic statistical information about water quality parameters, including average water quality value and data dispersion, through the statistical features of the average value. The relevant features are: Used to discover the correlation between water quality parameters. By calculating the correlation coefficient or correlation matrix between water quality parameters, the relationship between different parameters can be understood and possible correlation or influencing factors can be detected; The space Features: By calculating the spatial variation trend and spatial correlation of water quality parameters, we can understand the pollution degree or change of water bodies in different regions.

5. The ecological environment database construction system according to claim 1, characterized in that: The water quality data pollution detection and analysis model training module selects the water quality data pollution detection and analysis model of the regression model, divides the collected water quality data into a training set and a test set, uses the training set to train the selected machine learning model, and iteratively optimizes the model parameters to achieve a better fitting effect on the training set, and uses the test set to evaluate the performance of the model.

6. The ecological environment database construction system according to claim 1, characterized in that: The model application integration module will use the trained machine learning model to analyze the collected data, process the water quality ecological environment data at the river sewage outlet, and integrate it with the ecological environment database. When it is monitored that the water quality at the sewage outlet is polluted, the location of the sewage outlet is marked, and the source of the sewage is found through the sewage pipe of the sewage outlet, and the sewage source is cut off to ensure the water quality ecological environment.

Images