Smart rural environment monitoring and early warning system and method

Abstract

The invention relates to the field of monitoring and early warning, in particular to an intelligent rural environment monitoring and early warning system which comprises a sampling module used for sampling a water body in a rural environment and preparing a sample for testing; the fluid analysis module is used for analyzing the collected fluid sample and acquiring relevant characteristic parameters of the fluid by measuring the propagation speed or propagation time of the sound wave in the fluid; the data processing and evaluation module is used for receiving data of the sampling module and the fluid analysis module, processing and analyzing the data and evaluating the rural environment quality condition; and the early warning module is used for sending out an early warning signal according to a result of the data processing and evaluating module when the environment index exceeds a preset threshold value. The technical problems that the rural environment quality monitoring and early warning precision is low, the response speed is low, and the cost is high are solved. On the basis, the invention further provides an intelligent rural environment monitoring and early warning method.

Description

Technical Field

[0001] The present invention relates to the field of monitoring and early warning, and particularly to a smart rural environment monitoring and early warning system and method. Background Art

[0002] At present, with the rapid development of rural economy, the problem of environmental pollution has become increasingly prominent. In particular, water pollution poses a serious threat to the rural ecological environment and the health of residents. Traditional environmental monitoring methods often have disadvantages such as long monitoring cycles, high costs, and poor real-time performance, and cannot meet the needs of the rapidly changing rural environment. For example, a kind of ecological environment monitoring and early warning system and monitoring and early warning method disclosed in Chinese Patent Publication No.: CN109507382A. The present invention obtains the original data reflecting water quality monitoring indicators through an ammonia nitrogen detection probe; performs arithmetic operations and primary data calibration processing on the original data collected by the ammonia nitrogen monitoring probe; performs advanced calibration processing on the data after the primary data calibration processing to obtain the monitoring results and stores them; and displays the monitoring results to the monitoring terminal.

[0003] Therefore, it is particularly important to develop an efficient, real-time, and low-cost environmental monitoring and early warning system. As a non-contact and high-precision monitoring method, acoustic wave analysis technology has the advantages of fast response and low cost, and has broad application prospects in the field of environmental monitoring. Summary of the Invention

[0004] Therefore, in view of the above problems, the present invention proposes a smart rural environment monitoring and early warning system, which solves the technical problems of low accuracy, slow response speed, and high cost in rural environmental quality monitoring and early warning. Based on this, a smart rural environment monitoring and early warning method is also proposed.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A smart rural environment monitoring and early warning system, comprising: Sampling module: used to sample the water body in the rural environment and prepare samples for testing; Fluid analysis module: used to analyze the collected fluid samples, and obtain relevant characteristic parameters of the fluid by measuring the propagation speed or propagation time of acoustic waves in the fluid; Data processing and evaluation module: receives the data from the sampling module and the fluid analysis module, processes and analyzes the data, and evaluates the rural environmental quality status; Early warning module: according to the results of the data processing and evaluation module, when the environmental indicators exceed the preset threshold, an early warning signal is issued.

[0006] Further, the fluid analysis module includes: Acoustic wave transmitting unit: emits an acoustic wave signal with a set frequency and intensity into the fluid; Sound wave receiving unit: Receives the signal after the sound wave propagates in the fluid; Propagation characteristic calculation unit: Calculates the propagation speed or propagation time of the sound wave in the fluid, where the propagation speed is equal to the known distance traveled by the sound wave divided by the propagation time; Fluid characteristic analysis unit: Based on the sound wave-fluid characteristic correlation algorithm, analyzes the component, concentration, flow rate, and viscosity characteristic parameters of the fluid according to the propagation speed or propagation time of the sound wave.

[0007] Furthermore, the formula of the sound wave-fluid characteristic correlation algorithm is: ; Wherein, is the propagation speed of the sound wave in the fluid; is a known constant of the propagation speed of the sound wave in the reference state; is the influence coefficient of the th pollutant on the propagation speed of the sound wave; is the concentration of the th pollutant; is the number of types of pollutants; is the actual temperature of the fluid; is the actual pressure of the fluid; is the actual viscosity of the fluid; , , are the influence coefficients of the changes in temperature, pressure, and viscosity relative to the reference state on the propagation speed of the sound wave, respectively.

[0008] Furthermore, the determination method of the influence coefficients , , , is: Measuring the change data of the propagation speed of the sound wave under different conditions through experiments; Using the multiple nonlinear regression analysis method to fit the experimental data to obtain the numerical values of the influence coefficients , , , .

[0009] Furthermore, the environmental anomaly judgment algorithm adopted by the data processing and analysis module has the following formula: ; Wherein: is the environmental anomaly index, used to measure whether there are abnormal situations in the rural environment; is the propagation speed of the sound wave in the fluid; is a known constant for the sound wave propagation speed under the reference state; are the actual measured values of other environmental monitoring parameters; are the preset normal values of other environmental monitoring parameters; is the number of pollutant types; and and and are weight coefficients, obtained by training with experimental data, and are used to adjust the influence degree of different parameters on the environmental anomaly index; When the environmental anomaly index is greater than the preset threshold it is determined that there is an abnormal situation in the rural environment.

[0010] Furthermore, the determination method of the weight coefficients and and and includes the following steps: Data collection: Collect a large amount of data on the sound wave propagation speed and other environmental monitoring parameter data under different rural environmental states; Model construction: Construct an environmental anomaly judgment algorithm with the environmental anomaly index as the objective function; Parameter training: Use machine learning algorithms to train the weight coefficients and and and to minimize the error between the predicted environmental anomaly index of the model and the actual environmental anomaly situation; Parameter optimization: Optimize the trained weight coefficients through the cross-validation method to improve the accuracy and stability of the model.

[0011] A method applied to the intelligent rural environmental monitoring and early warning system includes the following steps: S1, Sampling: Take samples from the water body in the rural environment and prepare samples for testing; S2, Fluid analysis: Send the prepared fluid sample into the fluid analysis module, measure the sound wave propagation speed or propagation time, and analyze the composition, concentration, flow rate, and viscosity characteristic parameters of the fluid using the fluid property analysis unit; S3, Data processing and evaluation: Transmit the data generated during sampling and fluid analysis to the data processing and evaluation module, process and analyze the data, and evaluate the rural environmental quality status; S4, Early warning judgment: According to the analysis results of the data processing and evaluation module, compare with the preset environmental index thresholds. When an index exceeds the threshold, the early warning module issues an early warning signal.

[0012] Furthermore, after sampling, the sample is pre-treated, specifically including: S11, Filtration: For the sample, use filter membranes with different pore sizes to filter, removing impurities with larger particles. The pore size range of the filter membrane is 0.1μm - 100μm; S12, Dilution: Dilute the sample, and the dilution factor is 1 - 5 times; S13, Homogenization: Use a homogenizer for homogenization treatment to make the components in the sample evenly distributed. The stirring speed is 100 - 1000r / min, and the stirring time is 5 - 30min; S14, Concentration: Adopt the evaporation method for concentration to increase the concentration of the target substance in the sample. The evaporation temperature is 80 - 100°C to avoid the decomposition or volatilization of the target substance in the sample; S15, Preservation: Store the pre-treated sample in a brown glass bottle and store it in an environment of 2 - 10°C and away from light.

[0013] Furthermore, in step S2, when measuring the sound wave propagation speed or propagation time, conduct multiple measurements and take the average value.

[0014] By adopting the foregoing technical solutions, the beneficial effects of the present invention are: 1. Through the collaborative work of the sampling module, fluid analysis module, data processing and evaluation module, and early warning module, the real-time monitoring and early warning of the rural water environment are realized. It improves the timeliness and accuracy of environmental monitoring and helps to discover and handle environmental pollution problems in a timely manner.

[0015] 2. Through the analysis of the propagation characteristics of sound waves in fluids, the characteristic parameters of the fluid such as composition, concentration, flow rate, and viscosity can be accurately obtained. It improves the precision and sensitivity of environmental monitoring and helps to more accurately evaluate the environmental quality status.

[0016] 3. By introducing various influencing factors (such as pollutant concentration, temperature, pressure, viscosity, etc.), the relationship between the sound wave propagation speed and the fluid characteristics becomes more accurate and comprehensive. It improves the accuracy and reliability of environmental monitoring and provides a more scientific basis for environmental early warning.

[0017] 4. Through experimental measurement and multiple non-linear regression analysis, the influence coefficients can be accurately determined, improving the accuracy and applicability of the algorithm. It provides more accurate model parameters for environmental monitoring under different environmental conditions.

[0018] 5. By introducing the environmental anomaly index , comprehensively considering the sound wave propagation speed and other environmental monitoring parameters, improves the accuracy and reliability of environmental anomaly judgment. It helps to detect environmental anomalies in a timely manner and provides strong support for environmental management and decision-making.

[0019] 6. Through steps such as data collection, model construction, parameter training, and optimization, the weight coefficient can be accurately determined, improving the accuracy and stability of the environmental anomaly judgment algorithm. It provides more accurate model parameters for environmental anomaly judgment under different environmental conditions.

[0020] 7. Through steps such as sampling, fluid analysis, data processing and evaluation, and early warning judgment, systematic monitoring and early warning of the rural water environment are realized. It improves the efficiency and accuracy of environmental monitoring, and helps to detect and handle environmental pollution problems in a timely manner.

[0021] 8. Through steps such as filtration, dilution, homogenization, concentration, and preservation, the representativeness and stability of the samples are improved, which helps to obtain more accurate monitoring results. It reduces the interference of external factors on the monitoring results and improves the accuracy and reliability of environmental monitoring.

[0022] 9. By taking multiple measurements and averaging, the measurement error is reduced, and the measurement accuracy of the sound wave propagation speed or propagation time is improved. It provides more accurate data support for environmental monitoring and helps to improve the accuracy and reliability of environmental monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a schematic flow chart of the present invention.

[0024] Figure 2 is a schematic flow chart of the pretreatment of the sample after sampling of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0025] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0026] Refer to Figure 1 and Figure 2 , this embodiment provides an intelligent rural environmental monitoring and early warning system, including: Sampling module: used to sample water bodies in the rural environment and prepare samples for testing; Fluid analysis module: used to analyze the collected fluid samples, and obtain relevant characteristic parameters of the fluid by measuring the sound wave propagation speed or propagation time in the fluid; Data processing and evaluation module: receives the data from the sampling module and the fluid analysis module, processes and analyzes the data, and evaluates the rural environmental quality status; Early warning module: according to the results of the data processing and evaluation module, when the environmental indicators exceed the preset threshold, an early warning signal is issued.

[0027] Further, the fluid analysis module includes: An acoustic wave transmitting unit: transmitting an acoustic wave signal with a set frequency and intensity into the fluid; An acoustic wave receiving unit: receiving the signal after the acoustic wave propagates in the fluid; A propagation characteristic calculation unit: calculating the propagation speed or propagation time of the acoustic wave in the fluid, where the propagation speed is equal to the known distance traveled by the acoustic wave divided by the propagation time; A fluid characteristic analysis unit: based on an acoustic wave - fluid characteristic correlation algorithm, analyzing the component, concentration, flow rate, and viscosity characteristic parameters of the fluid according to the propagation speed or propagation time of the acoustic wave.

[0028] Among them, the frequency and intensity of the acoustic wave signal can be set according to actual needs. For example, the frequency of the acoustic wave signal is set to: 10 kHz to 500 kHz, and the intensity of the acoustic wave signal is set to: 100 W / m² to 1000 W / m². Preferably, the frequency of the acoustic wave signal is set to: 10 kHz to 200 kHz; the intensity of the acoustic wave signal is set to: 300 W / m² to 500 W / m².

[0029] Further, the formula of the acoustic wave - fluid characteristic correlation algorithm is: ; Where, is the propagation speed of the acoustic wave in the fluid; is a known constant of the propagation speed of the acoustic wave in the reference state; is the influence coefficient of the th pollutant on the propagation speed of the acoustic wave; is the concentration of the th pollutant; is the number of pollutant types; is the actual temperature of the fluid; is the actual pressure of the fluid; is the actual viscosity of the fluid; , , are the influence coefficients of the changes in temperature, pressure, and viscosity relative to the reference state on the propagation speed of the acoustic wave, respectively.

[0030] is the propagation speed of the acoustic wave in the reference state (such as 25°C, 1 atmosphere, pure water), set to 1500 m / s.

[0031] is the influence coefficient of the th pollutant on the propagation speed of the acoustic wave, determined through experiments.

[0032] Assume that the river is mainly affected by two pollutants, namely chemical oxygen demand (COD) and ammonia nitrogen (NH3-N), and their influence coefficients are = -0.5 and = -0.3.

[0033] is the concentration of the th pollutant, which is measured by the fluid analysis module. Assume that in a certain measurement, the COD concentration is 20 mg / L and the NH3-N concentration is 5 mg / L.

[0034] is the actual temperature of the fluid, which is measured by the temperature sensor and set to 28 °C.

[0035] is the actual pressure of the fluid, which is measured by the pressure sensor and set to 1.013×10^5 Pa (i.e., 1 atmosphere).

[0036] is the reference pressure, which is set to 1.013×10^5 Pa.

[0037] is the actual viscosity of the fluid, which is measured by the viscometer and set to 0.001 Pa*s.

[0038] 、 、 are the influence coefficients of the changes in temperature, pressure, and viscosity relative to the reference state on the acoustic wave propagation speed, respectively, which are obtained through experiments. Assume = 0.1, = 0.05, = 0.02.

[0039] Substituting into the calculation, the calculation result = 1489.55 m / s.

[0040] The determination method of the influence coefficients 、 、 、 is as follows: Measure the change data of the acoustic wave propagation speed under different conditions through experiments; Use the multiple nonlinear regression analysis method to fit the experimental data to obtain the numerical values of the influence coefficients 、 、 、 Here, the regression analysis method is a conventional algorithm in this field.

[0041] The environmental anomaly judgment algorithm adopted by the data processing and analysis module has the following algorithm formula: ; Where: is the environmental anomaly index, used to measure whether there are abnormal situations in the rural environment; is the propagation speed of sound waves in the fluid; is a known constant of the propagation speed of sound waves under the reference state; is the actual measured value of other environmental monitoring parameters; is the preset normal value of other environmental monitoring parameters; is the number of pollutant types; , , , are the weight coefficients, obtained through training with experimental data, and used to adjust the influence degree of different parameters on the environmental anomaly index.

[0042] When the environmental anomaly index is greater than the preset threshold , it is determined that there are abnormal situations in the rural environment.

[0043] The determination method of the weight coefficients , , , includes the following steps: Data collection: Collect a large amount of data on the propagation speed of sound waves and other environmental monitoring parameter data under different rural environmental conditions; Model construction: Taking the environmental anomaly index as the objective function, construct a model based on the above algorithm formula; Parameter training: Use machine learning algorithms to train the weight coefficients , , , so that the error between the environmental anomaly index predicted by the model and the actual environmental anomaly situation is minimized; Parameter optimization: Optimize the weight coefficients obtained through training by the cross-validation method to improve the accuracy and stability of the model.

[0044] Set = 1500 m / s (assumed to be the propagation speed of sound waves in pure water at 25°C).

[0045] Collect actual measurement data: In a certain measurement, the sound wave propagation speed = 1490 m / s, pH value = 7.3 (normal range 6.5 - 8.5), dissolved oxygen concentration = 5.8 mg / L (normal range ≥ 5 mg / L). Set weight coefficients: Obtained through training with experimental data = 0.5, = 2, = 0.3, = 1 (These coefficients are set according to specific experimental conditions and environmental characteristics).

[0046] Set normal values: = 7.0, = 5.5 mg / L.

[0047] Substitute into the formula to calculate the environmental anomaly index ≈ 0.03. Set the environmental anomaly index threshold = 0.02. Since > , the warning module issues a warning signal.

[0048] A method applied to the intelligent rural environment monitoring and warning system, including the following steps: S1, Sampling, sampling from the water body in the rural environment and preparing samples for testing; S2, Fluid analysis, sending the prepared fluid sample into the fluid analysis module, measuring the sound wave propagation speed or propagation time, and analyzing the composition, concentration, flow rate, and viscosity characteristic parameters of the fluid using the fluid characteristic analysis unit; S3, Data processing and evaluation, transmitting the data generated during sampling and fluid analysis to the data processing and evaluation module, processing and analyzing the data, and evaluating the rural environmental quality status; S4, Warning judgment, comparing the analysis results of the data processing and evaluation module with the preset environmental index threshold, and when an index exceeds the threshold, the warning module issues a warning signal.

[0049] Further, after sampling, preprocess the sample, specifically including: S11, Filtration: Filter the sample using filter membranes with different pore sizes to remove impurities with larger particles. The pore size range of the filter membrane is 0.1 μm - 100 μm; S12, Dilution: Dilute the sample, and the dilution multiple is 1 - 5 times; S13, Homogenization: Use a homogenizer for homogenization treatment to make the composition in the sample evenly distributed. The stirring speed is 100 - 1000 r / min, and the stirring time is 5 - 30 min; S14, Concentration: The evaporation method is adopted for concentration to increase the concentration of the target substance in the sample. The evaporation temperature is 80 - 100 °C to avoid the decomposition or volatilization of the target substance in the sample; S15, Preservation: The pretreated sample is stored in a brown glass bottle and kept in an environment of 2 - 10 °C in the dark.

[0050] Further, in step S2, when measuring the acoustic wave propagation speed or propagation time, multiple measurements are carried out and the average value is taken.

[0051] Although the present invention is specifically shown and described in conjunction with the preferred embodiments, those skilled in the art should understand that various changes can be made to the present invention in terms of form and details without departing from the spirit and scope of the present invention defined by the appended claims, and all of them fall within the protection scope of the present invention.

Claims

1. An intelligent rural environmental monitoring and early warning system, characterized in that, Including: Sampling module: used to sample water bodies in rural environments and prepare samples for testing; Fluid analysis module: used to analyze the collected fluid samples, and obtain relevant characteristic parameters of the fluid by measuring the propagation speed or propagation time of sound waves in the fluid; Data processing and evaluation module: receives the data from the sampling module and the fluid analysis module, processes and analyzes the data, and evaluates the rural environmental quality status; Early warning module: according to the results of the data processing and evaluation module, when the environmental indicators exceed the preset threshold, an early warning signal is issued.

2. The intelligent rural environment monitoring and early warning system according to claim 1, characterized in that, The fluid analysis module includes: Sound wave emitting unit: emits a sound wave signal with a set frequency and intensity into the fluid; Sound wave receiving unit: receives the signal after the sound wave propagates in the fluid; Propagation characteristic calculation unit: calculates the propagation speed or propagation time of sound waves in the fluid, where the propagation speed is equal to the known distance traveled by the sound wave divided by the propagation time; Fluid characteristic analysis unit: based on the sound wave-fluid characteristic correlation algorithm, analyzes the composition, concentration, flow rate, and viscosity characteristic parameters of the fluid according to the propagation speed or propagation time of the sound wave.

3. The intelligent rural environment monitoring and early warning system according to claim 2, characterized in that, The formula of the sound wave-fluid characteristic correlation algorithm is: ； Wherein, is the propagation speed of sound waves in the fluid; is a known constant of the propagation speed of sound waves under the reference state; is the influence coefficient of the th pollutant on the propagation speed of sound waves; is the concentration of the th pollutant; is the actual temperature of the fluid; is the actual pressure of the fluid; is the actual viscosity of the fluid; , , are the influence coefficients of the changes in temperature, pressure, and viscosity relative to the reference state on the propagation speed of sound waves, respectively.

4. The intelligent rural environment monitoring and early warning system according to claim 3, characterized in that, The influence coefficients , , , are determined by experimentally measuring the change data of the sound wave propagation speed under different conditions; The experimental data were fitted by using the multiple nonlinear regression analysis method to obtain the values of the influence coefficients , , , .

5. The intelligent rural environment monitoring and early warning system according to claim 1, characterized in that The environmental anomaly judgment algorithm adopted by the data processing and analysis module, and the formula of this algorithm is: ； Where: is the environmental anomaly index, which is used to measure whether there are anomalies in the rural environment; is the propagation speed of sound waves in a fluid; is a known constant for the propagation speed of sound waves under reference conditions; is the actual measured value of other environmental monitoring parameters; is the preset normal value for other environmental monitoring parameters; is the quantity of other environmental monitoring parameters; , , , are weight coefficients, obtained by training with experimental data, and are used to adjust the influence degree of different parameters on the environmental anomaly index; When the environmental anomaly index is greater than a preset threshold value it is determined that there is an abnormal situation in the rural environment.

6. A smart rural environmental monitoring and early warning system according to claim 5, characterized in that The weight coefficient , , , The determination method includes the following steps: Data collection: collect a large amount of sound wave propagation speed data and other environmental monitoring parameter data under different rural environmental conditions; Model construction: Using the environmental anomaly index as the objective function, construct an environmental anomaly judgment algorithm; Parameter training: Using machine learning algorithms to train the weight coefficients , , , to minimize the error between the environmental anomaly index predicted by the model and the actual environmental anomaly situation; Parameter optimization: optimize the weight coefficients obtained by training through the cross-validation method to improve the accuracy and stability of the model.

7. A method applied to the intelligent rural environment monitoring and early warning system according to any one of claims 1 to 6, characterized in that, Including the following steps: S1, Sampling, sample water bodies in rural environments and prepare samples for testing; S2, Fluid analysis, send the prepared fluid samples into the fluid analysis module, measure the propagation speed or propagation time of the sound wave, and use the fluid characteristic analysis unit to analyze the composition, concentration, flow rate, and viscosity characteristic parameters of the fluid; S3, Data processing and evaluation, transmit the data generated during sampling and fluid analysis to the data processing and evaluation module, process and analyze the data, and evaluate the rural environmental quality status; S4, Early warning judgment, according to the analysis results of the data processing and evaluation module, compare with the preset environmental indicator threshold, and when an indicator exceeds the threshold, the early warning module issues an early warning signal.

8. A method for intelligent rural environmental monitoring and early warning according to claim 7, characterized in that, After sampling, preprocess the samples, specifically including: S11, Filtration: filter the samples using filter membranes with different pore sizes to remove impurities with larger particles, and the pore size range of the filter membrane is 0.1μm - 100μm; S12, Dilution: dilute the samples, and the dilution factor is 1 - 5 times; S13, Homogenization: perform homogenization treatment using a homogenizer to make the composition distribution in the samples uniform, the stirring speed is 100 - 1000r / min, and the stirring time is 5 - 30min; S14, Concentration: adopt the evaporation method for concentration to increase the concentration of the target substance in the samples, and the evaporation temperature is 80 - 100°C to avoid the decomposition or volatilization of the target substance in the samples; S15, Preservation: Store the pre-treated sample in a brown glass bottle and keep it in an environment of 2 - 10°C away from light.

9. A method for intelligent rural environmental monitoring and early warning according to claim 7, characterized in that In step S2, when measuring the acoustic wave propagation speed or propagation time, make multiple measurements and take the average value.

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