Wastewater detection method and system for environmental monitoring based on Internet of Things

Abstract

The present invention relates to the field of wastewater monitoring technology, specifically to a wastewater detection method and system for environmental monitoring based on the Internet of Things, comprising the following steps: collecting wastewater pollutant concentration and water flow velocity data during the environmental monitoring process, calculating the pollutant concentration change rate, judging the pollutant diffusion state in combination with the water flow velocity trend, and obtaining the spatial diffusion state characteristics of the pollutants. In the present invention, through real-time monitoring and data analysis by Internet of Things devices, the determination of pollution sources and pollution diffusion trends is accelerated, especially the calculation and comparison of the change rates of pollutant concentrations in multiple regions, the cross-identification of pollutant sources is enhanced, and the positioning of pollution sources is made more accurate. Through dynamic tracking and reverse tracing technology, the flow direction of pollutants can be accurately determined. By analyzing the time series changes of pollutant concentrations, the abnormal change intervals of pollutant concentrations can be recorded and identified, further enhancing the accuracy and timeliness of environmental risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater monitoring, and in particular to a wastewater detection method and system for environmental monitoring based on the Internet of Things. Background Art

[0002] The field of wastewater monitoring encompasses the application of technologies for quality analysis and control of wastewater generated during production and daily life. Its core focus is on assessing potential risks to the environment and public health by monitoring the chemical substances, biological components, and physical properties of wastewater. This field encompasses a variety of monitoring methods, including spectral analysis, biochemical testing, heavy metal detection, and organic pollutant analysis. Through technical collaboration, a systematic wastewater monitoring network has been established to track wastewater treatment effectiveness in real time and ensure that water quality meets regulatory requirements.

[0003] The wastewater testing methods for environmental monitoring refer to a series of wastewater quality testing technologies designed specifically for environmental monitoring purposes. The patent addresses technical matters such as optimizing sampling methods, improving testing accuracy, and accelerating testing speed. Specific technical approaches include sample pretreatment, standardized testing procedures, and quantitative analysis of specific pollutants. These technical approaches collectively constitute the core content of the patent, providing reliable technical support for environmental monitoring.

[0004] Existing wastewater monitoring technologies face several key limitations in actual operation. The monitoring methods focus on static quality analysis and lack real-time monitoring of changes in pollutant concentrations and water flow dynamics, resulting in an inability to respond promptly to rapid changes in pollution incidents. Traditional technologies are inefficient in identifying pollution sources in multiple regions, making it difficult to quickly and accurately determine the source and flow of pollutants. This lack of efficiency has led to the expansion of environmental pollution incidents and increased the difficulty and cost of environmental remediation. Existing technologies often require longer processing times and more manual intervention when processing complex environmental data, which limits their application efficiency in emergency environmental monitoring. Technical limitations not only affect the timeliness and accuracy of pollution monitoring, but also lead to incomplete assessments of environmental risks, affecting the effectiveness of environmental management. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a wastewater detection method and system for environmental monitoring based on the Internet of Things.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a wastewater detection method for environmental monitoring based on the Internet of Things, comprising the following steps:

[0007] S1: Collect wastewater pollutant concentration and water flow velocity data during environmental monitoring, calculate the pollutant concentration change rate, and determine the pollutant diffusion state based on the water flow velocity trend to obtain the pollutant spatial diffusion state characteristics;

[0008] S2: Based on the spatial diffusion state characteristics of the pollutants, the pollutant component characteristic data of the IoT water monitoring equipment is used to identify the changes in the concentration gradient direction of the pollutant components, and the pollutant concentration change rate is matched with the water flow direction to obtain the cross-regional correlation identification results of the pollutants;

[0009] S3: Based on the cross-regional association identification results of the pollutants, the offset trajectory of the wastewater vortex center is identified, spatial position matching is performed according to the flow path of the pollutants in the wastewater, and the source of the pollutants flowing into the water body is traced back to generate a dynamic traceability trajectory of the pollutant flow direction;

[0010] S4: Based on the dynamic traceability trajectory of the pollutant flow, extract the data of the pollutant concentration change over time series, compare the pollutant concentration change rate with the normal pollution diffusion rate data, record the abnormal change interval of the pollutant concentration, identify the abnormal situation of the pollutant source, and obtain the abnormal record of the pollutant source;

[0011] The steps for obtaining the dynamic traceability trajectory of the pollutant flow are specifically as follows:

[0012] S311: Analyzing the change trend of water flow velocity at differentiated watershed nodes based on the cross-regional association identification results of the pollutants, calculating the water flow velocity change rate of multiple watershed nodes, and screening watershed nodes with fluctuating water flow velocities to obtain water flow velocity change information for the watershed nodes;

[0013] S312: Analyze the position offset trajectory of the wastewater vortex center based on the water flow velocity change information of the watershed node, identify the vortex center coordinates at the differentiated time nodes, and use the formula:

[0014] ;

[0015] Calculate the swirl center deviation rate value, analyze the change trend of the swirl center deviation rate, and obtain the wastewater swirl center deviation trajectory data;

[0016] in, represents the swirl center deviation rate, 、 Representative The coordinates of the swirl center of the position, 、 Representative The coordinates of the swirl center of the position, Representative The time node of the location, Representative The time node of the location, is the total number of monitoring points;

[0017] S313: Using the wastewater vortex center offset trajectory data, spatial position matching is performed according to the pollutant flow path, the source area of the pollutant flowing into the water body is identified, and a dynamic traceability trajectory of the pollutant flow direction is obtained.

[0018] As a further solution of the present invention, the spatial diffusion state characteristics of the pollutants include the concentration change rate, water flow acceleration trend, and spatial diffusion pattern. The cross-regional association identification results of the pollutants include the component feature similarity ratio, concentration gradient direction change, and water flow direction matching information. The dynamic traceability trajectory of the pollutant flow direction includes the offset of the wastewater vortex center, the pollutant flow path, and the source reverse tracking results. The pollutant source abnormality records include the concentration change rate comparison results, abnormal change interval, and source abnormality.

[0019] As a further solution of the present invention, the steps for obtaining the spatial diffusion state characteristics of the pollutants are specifically as follows:

[0020] S111: Collect wastewater pollutant concentration and water flow velocity data during environmental monitoring, extract the initial concentration, flow velocity data and component characteristics of pollutants, identify the diffusion of pollutants transported by water flow, and obtain the basic diffusion rate of pollutants;

[0021] S112: Based on the basic diffusion rate of the pollutants and in combination with the water velocity data, identifying the change of the pollutant concentration under the influence of the differentiated water flow acceleration, calculating the concentration change rate of the pollutants under the influence of the water flow acceleration, analyzing the change trend of the pollutant concentration during the water flow transport process, and obtaining the pollutant concentration change trend;

[0022] S113: By combining the pollutant concentration change trend with the water flow velocity data, the pollutant diffusion state is judged, the concentration change characteristics within the pollutant diffusion area are extracted, and the diffusion range of the pollutants under the differentiated water flow velocity and acceleration changes is analyzed to obtain the pollutant spatial diffusion state characteristics.

[0023] As a further solution of the present invention, the steps for obtaining the cross-regional association identification results of pollutants are specifically as follows:

[0024] S211: Based on the spatial diffusion state characteristics of the pollutants, the directional change trend of the pollutant concentration gradient is analyzed, the pollutant concentration change rate is calculated, and the matching degree between the pollutant concentration change rate and the water flow direction is evaluated in combination with the water flow direction data, using the formula:

[0025] ;

[0026] Calculate the pollutant concentration gradient change matching value, and classify the differentiated pollutant concentration gradient direction changes to obtain the pollutant concentration gradient matching data;

[0027] in, Represents the matching value of the pollutant concentration gradient change, Representative Pollutant concentration at the location, Representative Pollutant concentration at the location, represent The spatial distance of the location, Represents water flow The flow rate at the location, is the total number of monitoring points;

[0028] S212: Analyze the matching between the pollutant concentration change rate and the water flow direction through the pollutant concentration gradient matching data, determine whether there is any abnormal cross-change in the pollutant concentration change, screen the cross-regional correlation characteristics of pollutants, and obtain the cross-regional correlation identification results of pollutants.

[0029] As a further solution of the present invention, the method further includes step S5:

[0030] S5: Using the abnormal records of pollutant sources, the flow inertia and flow gradient of the environmental monitoring wastewater are detected, and the probability of pollution diffusion path is deduced to determine the continued diffusion risk of pollutants, analyze the distribution range of pollutant diffusion areas, and obtain the wastewater pollution diffusion assessment results;

[0031] The wastewater pollution diffusion assessment results include water flow inertia analysis results, flow direction gradient detection information, continuous diffusion risk judgment results, and regional distribution range analysis results.

[0032] As a further embodiment of the present invention, the steps for obtaining the wastewater pollution diffusion assessment results are specifically as follows:

[0033] S511: Detecting the water flow inertia and flow direction gradient of the environmental monitoring wastewater based on the abnormal records of the pollutant source, evaluating the influence of the water flow inertia on the diffusion of pollutants, calculating the directional offset of the flow direction gradient, and obtaining the pollutant diffusion directional offset;

[0034] S512: Analyzing the mobility of the pollution diffusion path using the pollutant diffusion direction offset, and calculating the probability value of the pollution diffusion path by combining the water flow inertia effect and flow gradient characteristics of the differentiated path, thereby obtaining pollution diffusion path probability data;

[0035] S513: Based on the pollution diffusion path probability data, determine the continued diffusion risk of pollutants, analyze the coverage of pollutant diffusion, and identify the pollution diffusion impact of risk areas to obtain wastewater pollution diffusion assessment results.

[0036] The wastewater detection system for environmental monitoring based on the Internet of Things is used to perform the above-mentioned wastewater detection method for environmental monitoring based on the Internet of Things, and the system includes:

[0037] The pollutant monitoring module obtains the pollutant concentration, water flow velocity and pollutant component characteristic data of wastewater for environmental monitoring, calculates the pollutant concentration change rate, extracts the water flow acceleration trend, calls the water flow acceleration trend and the pollutant concentration change rate, analyzes the time series characteristics of the pollutant diffusion state, and obtains the spatial diffusion state characteristics of the pollutants;

[0038] The water flow direction identification module extracts the pollutant component characteristic data from the IoT water body online monitoring equipment based on the spatial diffusion state characteristics of the pollutants, analyzes the matching between the pollutant concentration change rate and the water flow direction, and obtains the cross-regional correlation identification results of pollutants;

[0039] The deviation trajectory identification module calls the cross-regional correlation identification results of the pollutants, analyzes the changing trend of water flow velocity at the intersection of differentiated watersheds, identifies the deviation trajectory of the core point of the wastewater vortex, evaluates the spatial matching degree of the pollutant flow path, and obtains the dynamic traceability trajectory of the pollutant flow direction;

[0040] The source anomaly detection module calls the dynamic traceability trajectory of the pollutant flow, extracts the data of the pollutant concentration change over time, compares the pollutant concentration change rate with the pollution diffusion rate in the monitoring interval, identifies the abnormal change information of the pollutant, and obtains the abnormal record of the pollutant source;

[0041] The wastewater risk assessment module calls the abnormal records of the pollutant sources, detects the water flow inertia and flow direction gradient of the wastewater used for environmental monitoring, calculates the probability value of the pollution diffusion path, determines the pollutant diffusion risk, and obtains the wastewater pollution diffusion assessment result.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are:

[0043] In the present invention, by collecting pollutant concentration change and water flow velocity data, combined with the characteristic analysis of pollutant components, the ability to identify pollution sources and diffusion paths is improved. Real-time monitoring and data analysis of Internet of Things devices are used to accelerate the determination of pollution sources and pollution diffusion trends. In particular, the calculation and comparison of the rate of change of pollutant concentrations in multiple regions enhances the cross-identification of pollutant sources, making the positioning of pollution sources more accurate. Through dynamic tracking and reverse tracing technology, the flow direction of pollutants can be accurately determined, providing a scientific basis for subsequent environmental remediation and preventive measures. By analyzing the time series changes in pollutant concentrations, the abnormal change intervals of pollutant concentrations can be accurately recorded and identified, further enhancing the accuracy and timeliness of environmental risk assessments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0045] Figure 2 This is a flow chart for obtaining the spatial diffusion state characteristics of pollutants in the present invention;

[0046] Figure 3 A flowchart for obtaining cross-regional correlation identification results of pollutants in the present invention;

[0047] Figure 4 A flow chart for obtaining the dynamic traceability trajectory of pollutant flow in the present invention;

[0048] Figure 5 A flowchart of obtaining abnormal records of pollutant sources in the present invention;

[0049] Figure 6 The figure is a flow chart of obtaining the wastewater pollution diffusion assessment results in the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0052] See also Figure 1 The present invention provides a technical solution, a wastewater detection method for environmental monitoring based on the Internet of Things, comprising the following steps:

[0053] S1: Collect wastewater pollutant concentration and water flow velocity data during environmental monitoring, calculate the pollutant concentration change rate, and determine the pollutant diffusion state based on the water flow velocity trend to obtain the pollutant spatial diffusion state characteristics;

[0054] S2: Based on the spatial diffusion characteristics of pollutants, the pollutant component characteristic data of IoT water monitoring equipment is used to identify the changes in the concentration gradient direction of pollutant components, match the pollutant concentration change rate with the water flow direction, and obtain the cross-regional correlation identification results of pollutants;

[0055] S3: Based on the results of cross-regional pollutant correlation identification, analyze the changing trend of water flow velocity at different watershed nodes, identify the offset trajectory of the wastewater vortex center, match the spatial position of the pollutant flow path of the wastewater, reversely trace the source of the pollutant flowing into the water body, and generate a dynamic traceability trajectory of the pollutant flow direction;

[0056] S4: Based on the dynamic traceability of pollutant flow, extract the data of pollutant concentration changes over time, compare the pollutant concentration change rate with the normal pollution diffusion rate data, record the abnormal change interval of pollutant concentration, identify the abnormal source of pollutants, and obtain the abnormal record of pollutant source;

[0057] S5: By recording abnormal pollutant sources, detecting the flow inertia and flow gradient of environmental monitoring wastewater, probabilistically deriving the pollution diffusion path, determining the continued diffusion risk of pollutants, analyzing the distribution range of pollutant diffusion areas, and obtaining wastewater pollution diffusion assessment results;

[0058] The spatial diffusion state characteristics of pollutants include the concentration change rate, water flow acceleration trend, and spatial diffusion pattern. The cross-regional correlation identification results of pollutants include the component characteristic similarity ratio, concentration gradient direction change, and water flow direction matching information. The dynamic traceability trajectory of pollutant flow includes the offset of the wastewater vortex center, the pollutant flow path, and the source reverse tracking results. The abnormal records of pollutant sources include the concentration change rate comparison results, abnormal change interval, and source abnormality. The wastewater pollution diffusion assessment results include water flow inertia analysis results, flow gradient detection information, continuous diffusion risk judgment results, and regional distribution range analysis results.

[0059] See also Figure 2 , the specific steps for obtaining the spatial diffusion state characteristics of pollutants are:

[0060] S111: Collect wastewater pollutant concentration and water flow velocity data during environmental monitoring, extract the initial concentration, flow velocity data and component characteristics of pollutants, identify the diffusion of pollutants transported by water flow, and obtain the basic diffusion rate of pollutants;

[0061] When setting up monitoring points, it is necessary to select a suitable monitoring area and set up a sampling point at a fixed distance within the watershed. If in an urban river, set up a monitoring point every 10 meters to ensure coverage of the entire polluted area. Use an automatic water quality sampler to collect water samples in real time, collect 500 ml of samples each time, and record the sampling time and location information. All water samples should be stored in a low-temperature environment of 4°C and sent to the laboratory for analysis. The concentration of pollutants can be determined by using a spectrophotometer for total organic carbon (TOC) determination, and total nitrogen (TN) and total phosphorus (TP) can be determined by chemical reagent colorimetry. The pollutant unit is set to mg / L. If the TOC value at a monitoring point is 5.2 mg / L, TN is 2.1 mg / L, T P is 0.8 mg / L. The water flow velocity can be measured by using an ultrasonic flow meter to record the flow velocity data in m / s. If the water flow velocity measured at a certain measuring point is 0.85 m / s, the pollutant component characteristics can be quantitatively analyzed by gas chromatography-mass spectrometry. After sorting the data of each monitoring point, a pollutant transport parameter matrix is established. The rows of the matrix correspond to different monitoring points, and the columns correspond to pollutant concentrations, water flow velocities, and component characteristic data. After the matrix is constructed, the basic diffusion rate of pollutants transported with water flow is calculated. The concentration change rate of pollutants can be defined as the concentration change between adjacent monitoring points divided by the distance between the two points. If the sampling point spacing in a certain water section is 10 meters, the basic diffusion rate of pollutants can be calculated.

[0062] If the TOC concentration detected at the first monitoring point is 5.2 mg / L and the TOC concentration at the second monitoring point is 6.1 mg / L, the concentration change rate is calculated as:

[0063] (6.1-5.2) / 10=0.09;

[0064] If the TOC at the third monitoring point is 7.0 mg / L, the rate of change is calculated as:

[0065] (7.0-6.1) / 10=0.09;

[0066] The concentration change rate of all monitoring points is averaged, that is, the basic diffusion rate of pollutants is calculated as: (0.09+0.09) / 2=0.09;

[0067] Obtain the basic diffusion rate of pollutants.

[0068] S112: Based on the basic diffusion rate of pollutants and combined with water velocity data, identify the changes in pollutant concentration under the influence of differentiated water flow acceleration, calculate the concentration change rate of pollutants under the influence of water flow acceleration, analyze the change trend of pollutant concentration during water flow transportation, and obtain the pollutant concentration change trend;

[0069] S113: Determine the pollutant diffusion state by combining pollutant concentration change trends with water velocity data, extract concentration change characteristics within the pollutant diffusion area, analyze the diffusion range of pollutants under different water velocity and acceleration changes, and obtain the spatial diffusion state characteristics of pollutants;

[0070] Combined with the water flow acceleration trend data, the pollutant diffusion state is judged and the pollutant diffusion threshold is set. The ratio of the pollutant concentration change rate to the water flow velocity is used as the measurement basis. The judgment standard of the pollutant diffusion state uses the ratio of the concentration change rate to the water flow velocity as a reference. The higher the pollutant concentration change rate, the stronger the pollutant diffusion trend. According to the actual monitoring data analysis, the pollutant diffusion threshold is set to 0.05. If the calculated value is greater than the threshold, the pollutant diffuses faster, otherwise it diffuses slower. The pollutant diffusion coefficient is calculated. The pollutant diffusion coefficient calculation formula is as follows:

[0071] ;

[0072] The pollutant concentration change rate calculated in the previous step and water flow speed Substitute into the calculation:

[0073] ;

[0074] This value is much larger than the set threshold, so the pollutant diffusion rate is faster. The concentration change characteristics within the pollutant diffusion area are screened, and the diffusion range of pollutants under different water flow velocities and acceleration changes are further analyzed to obtain the spatial diffusion state characteristics of pollutants.

[0075] See also Figure 3 ,The specific steps for obtaining the cross-regional correlation identification results of pollutants are:

[0076] S211: Based on the spatial diffusion characteristics of pollutants, analyze the directional change trend of the pollutant concentration gradient, calculate the pollutant concentration change rate, and combine it with the water flow direction data to evaluate the matching degree between the pollutant concentration change rate and the water flow direction. The formula is:

[0077] ;

[0078] Calculate the pollutant concentration gradient change matching value, and classify the differentiated pollutant concentration gradient direction changes to obtain the pollutant concentration gradient matching data;

[0079] in, Represents the matching value of the pollutant concentration gradient change, Representative Pollutant concentration at the location, Representative Pollutant concentration at the location, represent The spatial distance of the location, Represents water flow The flow rate at the location, is the total number of monitoring points;

[0080] The formula combines the rate of change of concentration gradient with the water velocity to quantify the diffusion trend of pollutants in water bodies. It also numerically characterizes whether pollutants diffuse steadily with the water flow or are interfered with by residual factors. It can effectively identify whether the source of pollutants is a single diffusion or is affected by the input of pollution sources.

[0081] Formula parameter value acquisition process:

[0082] Pollutant concentration ( ): Collected through water body online monitoring equipment, such as water quality sensors can measure the real-time concentration of pollutants (unit );

[0083] Spatial distance ( ): By setting the distance between monitoring points, according to the water body monitoring point layout plan, all monitoring points are set to be 100 meters apart;

[0084] Water flow rate ( ): Measured by a flow meter, it can be combined with a hydrological station or buoy-type flow monitoring device to set the measured data as follows: 、 、 、 、 ;

[0085] Obtain pollutant concentration data collected by water monitoring equipment and divide it according to spatial location. For example, set up multiple monitoring points in a river section, obtain the instantaneous concentration value of the pollutant at each monitoring point, and set the concentration of pollutant A measured at 5 monitoring points to be 、 、 、 、 Then, calculate the concentration gradient between each point. The gradient is defined as the concentration difference between two adjacent monitoring points divided by the distance between them. If the distance between each two monitoring points is 100 meters, the gradient is calculated as:

[0086] ;

[0087] By analogy, all gradient values are calculated to obtain the diffusion trend of pollutants along the water body;

[0088] Formula calculation example:

[0089] The concentrations of pollutant A at five monitoring points in the water body are set as follows:

[0090] ;

[0091] Distance between adjacent monitoring points , water flow velocity:

[0092] ;

[0093] Substituting into the formula:

[0094] ;

[0095] ;

[0096] The results show that the matching value of the pollutant concentration gradient change is 0.00546. A threshold for the concentration gradient change is set during the calculation of the concentration gradient matching. This threshold is used to determine whether the pollutant diffusion rate is within the normal range. The setting basis is the pollutant diffusion under similar water flow rates in the monitoring data. The stable range of concentration gradient change for this type of pollutant in the water body with a flow rate of v=0.3 to v=0.4 in the monitoring data is set to 0.002 to 0.007. Therefore, the pollutant concentration gradient matching threshold is set to 0.002. Values below this value indicate abnormal pollution sources. As the basis for cross-contamination judgment, it is found that this value exceeds the threshold, indicating that the diffusion trend of the pollutant basically matches the water flow direction, and there is no abnormal cross-source. If the matching value calculated in a certain area is lower than 0.002, there is external pollution input, and further analysis of the pollutant source and the pollution characteristics of the associated area is required.

[0097] S212: Analyze the matching between the pollutant concentration change rate and the water flow direction through the pollutant concentration gradient matching data, determine whether there is any abnormal cross-continuity in the pollutant concentration change, screen the cross-regional correlation characteristics of pollutants, and obtain the cross-regional correlation identification results of pollutants;

[0098] Extract the pollutant concentration gradient matching data and compare it with the established flow direction of the water body. If the water flow direction is from monitoring point 1 to monitoring point 5, theoretically, the pollutant concentration gradient change should show a monotonically increasing or decreasing trend. However, if a reverse change occurs at a certain point (for example, the concentration at monitoring point 3 is lower than that at monitoring point 2), it means that there is an external pollution input source at that point. Calculate the characteristic parameters of cross-regional association of pollutants, set a threshold of 0.002, and if the pollutant concentration gradient matching value of a certain section of water area is lower than this threshold, it is determined to be an abnormal area. The matching value calculated at monitoring point 3 is 0.0018, which is lower than the set threshold, and the area is marked as having a cross-source of pollutants. Combined with the hydrological monitoring data, check whether there is a sewage outlet or tributary inflow in the area, further analyze the source of pollutants, and obtain the cross-regional association identification results of pollutants.

[0099] See also Figure 4 ,The specific steps for obtaining the dynamic traceability trajectory of pollutant flow are:

[0100] S311: Based on the cross-regional correlation identification results of pollutants, the changing trend of water flow velocity at different watershed nodes is analyzed, the water flow velocity change rate of multiple watershed nodes is calculated, and the watershed nodes with fluctuating water flow velocity are screened to obtain the water flow velocity change information of the watershed nodes;

[0101] The water flow velocity data of multiple river basin nodes are extracted. The water flow velocity of each river basin node in different time periods needs to be continuously monitored to ensure the integrity and reliability of the data. The monitoring time interval is set to 5 minutes, and the total monitoring time is 1 hour. Each river basin node obtains 12 water flow velocity data points in the entire period. The water flow velocity is calculated based on the flow meter measurement data and the cross-section average flow velocity formula. The water flow velocity change rate of each node is calculated as the difference in water flow velocity between adjacent time points divided by the time interval. The calculation results are used to analyze the changing trend of water flow velocity, screen river basin nodes whose fluctuation amplitude exceeds the set threshold, and record their corresponding time and space coordinates. The water flow velocity fluctuation threshold is set based on the annual monitoring data in the basin. The specific calculation method is to take the 90th percentile of all water flow velocity change rate data in the past 365 days. The number indicates that the rate of change of water flow velocity is lower than this value in 90% of the time. This is used as the criterion for judging abnormal fluctuations. The threshold is set to the 90% quantile of the water flow velocity change rate. Through the analysis of data throughout the year, the 90% quantile change rate of the basin node is set to 0.3. Then, when the water flow velocity of a certain node at a certain moment changes from 0.5m / s to 0.9m / s, the calculated change rate is (0.9-0.5) / 5=0.08. Because it is lower than the threshold, the node is not judged as abnormal. If the water flow velocity changes from 0.5m / s to 2.0m / s, the calculated change rate is (2.0-0.5) / 5=0.3, which is equal to the threshold, then the node is classified as an abnormal basin node. According to the water flow change trend of each node, the water flow velocity change distribution map is drawn, and the distribution range and affected area of the abnormal area are identified to obtain the water flow velocity change information of the basin node.

[0102] S312: Based on the water flow velocity change information at the watershed nodes, analyze the position offset trajectory of the wastewater vortex center and identify the vortex center coordinates at the differentiated time nodes using the formula:

[0103] ;

[0104] Calculate the swirl center deviation rate value, analyze the change trend of the swirl center deviation rate, and obtain the wastewater swirl center deviation trajectory data;

[0105] in, represents the swirl center deviation rate, 、 Representative The coordinates of the swirl center of the position, 、 Representative The coordinates of the swirl center of the position, Representative The time node of the location, Representative The time node of the location, is the total number of monitoring points;

[0106] Parameter meaning and calculation derivation process:

[0107] The spatial coordinates of the vortex center are collected by a high-precision GPS system. The data of each time point are collected once every 5 minutes. The total monitoring time is set to 1 hour. The coordinates of the vortex center at 12 time points are obtained, and the offset distance between adjacent time points is calculated, and the offset rate is calculated. In the actual calculation, the starting coordinates of a vortex center are set as , the coordinates after 5 minutes become , the coordinates after 10 minutes become , calculate the migration rate as follows:

[0108] Compute the drift rate for the first time interval:

[0109] ;

[0110] Compute the drift rate for the second time interval:

[0111] ;

[0112] ;

[0113] Accumulating all time points, the total offset rate is:

[0114] ;

[0115] The results show that during the observation time, the average deviation rate of the wastewater vortex center is 8.45, which can be used to analyze the flow trend of pollutants and determine whether the source of pollutants is stable or has abnormal changes.

[0116] S313: Using the wastewater cyclone center offset trajectory data, spatial position matching is performed according to the pollutant flow path to identify the source area of the pollutant flowing into the water body and obtain a dynamic traceability trajectory of the pollutant flow direction;

[0117] The spatial distribution data of pollutant flow paths are obtained. Based on the time series monitoring data of pollutant concentrations, the time interval is set to 5 minutes, and the concentration gradient changes of pollutants at different time points are calculated. Pollutant concentration data are collected by water quality sensors. Multiple monitoring sections are set at each sampling point to obtain pollutant concentration data at different depths, and the concentration gradients of pollutants in the horizontal and vertical directions are calculated. The concentration gradient is calculated as the difference between the pollutant concentrations at adjacent monitoring points divided by the spatial distance. The calculation result is used to determine the diffusion direction of pollutants and compared with the water flow direction data to determine whether pollutants migrate with the water flow direction. If the direction of the pollutant concentration gradient is consistent with the water flow direction, the pollutants are determined to be driven by hydrodynamics. If the direction of the pollutant concentration gradient is opposite to the water flow direction, the pollutants are determined to have an external input source. The pollutant concentration gradient threshold is set to the 90th percentile of the pollutant flow period in the basin in the past year. This quantile indicates that the pollutant concentration gradient is lower than this value for 90% of the time. As the basis for abnormal judgment, the pollutant concentration gradient threshold in a certain area is set to 0.02. If the pollutant concentration at a certain measuring point changes from 0.8 to 1.2, and the distance between adjacent points is 20m, the concentration gradient is calculated as:

[0118] (1.2-0.8) / 20=0.02;

[0119] The results show that the concentration gradient is equal to the threshold, and it is determined that the pollutant concentration changes in this area are more drastic. A pollutant flow source tracing analysis is carried out. Based on the results of the pollutant source analysis, the pollutant source area is determined, and combined with the pollutant flow path, a dynamic pollutant flow tracing trajectory is established.

[0120] See also Figure 5 , the specific steps for obtaining the wastewater pollution diffusion assessment results are as follows:

[0121] S511: Detect the flow inertia and flow gradient of environmental monitoring wastewater through abnormal records of pollutant sources, evaluate the impact of flow inertia on pollutant diffusion, calculate the directional offset of the flow gradient, and obtain the pollutant diffusion directional offset;

[0122] It is necessary to measure the flow characteristics of wastewater used for environmental monitoring, including changes in water flow inertia and flow gradient. In the actual monitoring process, flow velocity sensors can be deployed at multiple points at the pollutant discharge port and downstream. By monitoring the change of flow velocity over time, the inertia strength of the water flow can be calculated. Water flow inertia can be defined as the absolute value of the change in flow velocity per unit time. At a certain monitoring point, if the water flow increases from 0.5m / s to 1.2m / s within 10 seconds, the water flow inertia can be calculated as:

[0123] ;

[0124] The flow velocity data measured at multiple monitoring points can be used to calculate the flow gradient. The flow gradient can be measured by the rate of change of flow velocity between adjacent monitoring points. If the flow velocity at the upstream monitoring point is 1.2 m / s and the flow velocity at the downstream monitoring point is 0.8 m / s, and the distance between the two points is 50 meters, the flow gradient can be calculated as:

[0125] ;

[0126] From this, we can get the trend of wastewater flow direction. In this process, it is necessary to set a threshold for the flow gradient change rate to determine the area where pollutants may spread. The setting of this threshold is based on the mean and standard deviation of the flow gradient at the monitoring point. If the flow gradient data monitored in a certain area are -0.01, -0.015, -0.005, 0.002, and -0.012, the mean is calculated as:

[0127] ;

[0128] The standard deviation is calculated as:

[0129] ;

[0130] Therefore, the flow gradient change rate threshold can be: ;

[0131] It indicates that there is a risk of pollution diffusion in areas exceeding this value. At the same time, the offset of the pollutant diffusion direction is further determined by combining the spatial distribution of the flow gradient. The calculation method can be calculated by the change of the flow gradient vector. If the flow gradient of different monitoring points in a wastewater basin varies greatly, the average offset angle can be calculated. If the flow directions in a certain area are set to 0°, 15°, and 25° respectively, the average offset angle is calculated as:

[0132] ;

[0133] Obtain the pollutant diffusion direction offset.

[0134] S512: Using the pollutant diffusion direction offset, analyze the mobility level of the pollution diffusion path, combine the water flow inertia effect and flow gradient characteristics of the differentiated path, calculate the probability value of the pollution diffusion path, and obtain the pollution diffusion path probability data;

[0135] S513: Based on the probability data of pollution diffusion paths, determine the risk of continued diffusion of pollutants, analyze the coverage of pollutant diffusion, and identify the pollution diffusion impact of risk areas to obtain the wastewater pollution diffusion assessment results;

[0136] To further determine the risk of continued spread of pollutants and analyze the spatial coverage of pollutants, after obtaining the probability data of the diffusion path, it is necessary to compare it with the actual monitored pollutant concentration data. In a certain area, if the measured pollutant concentration is 0.6 mg / L and the background pollutant concentration in the area is 0.2 mg / L, the relative increase of the pollutant is calculated as:

[0137] ;

[0138] This indicates that the pollution concentration is 2 times higher than the background value. At the same time, the pollution diffusion coverage can be calculated by interpolation based on the pollution concentration distribution of each monitoring point in the diffusion area. At 5 monitoring points in a certain area, the measured pollutant concentrations are 0.6, 0.5, 0.7, 0.4, and 0.6 respectively. The average pollution concentration is calculated as:

[0139] ;

[0140] Combined with the probability level of pollution diffusion path, the spatial range of pollution diffusion is estimated. At this time, it is necessary to set a pollutant concentration baseline value to determine whether the pollutants have reached the high-risk diffusion area. The pollutant concentration baseline value can refer to the local environmental standards. If the standard limit of wastewater pollutants in a certain place is set to 0.3 mg / L, then if the pollutant concentration exceeds 1.5 times of this value, that is:

[0141] ;

[0142] The area can be considered as a high-risk pollution area, and high-risk diffusion areas are screened. If the diffusion path probability of a certain area is higher than 3.0 and the pollutant concentration exceeds 0.45 mg / L, the area is judged as a high-risk area, and the wastewater pollution diffusion assessment result is obtained.

[0143] The wastewater detection system for environmental monitoring based on the Internet of Things is used to implement the above-mentioned wastewater detection method for environmental monitoring based on the Internet of Things. The system includes:

[0144] The pollutant monitoring module obtains the pollutant concentration, water flow velocity and pollutant component characteristic data of wastewater for environmental monitoring, calculates the pollutant concentration change rate, extracts the water flow acceleration trend, calls the water flow acceleration trend and the pollutant concentration change rate, analyzes the time series characteristics of the pollutant diffusion state, and obtains the spatial diffusion state characteristics of the pollutants;

[0145] The water flow direction identification module extracts pollutant component characteristic data from IoT water body online monitoring equipment based on the spatial diffusion state characteristics of pollutants, analyzes the matching between the pollutant concentration change rate and water flow direction, and obtains cross-regional correlation identification results of pollutants;

[0146] The deviation trajectory identification module uses the cross-regional correlation identification results of pollutants to analyze the changing trend of water flow velocity at the intersection of differentiated watersheds, identify the deviation trajectory of the core point of the wastewater vortex, evaluate the spatial matching degree of the pollutant flow path, and obtain the dynamic traceability trajectory of the pollutant flow direction;

[0147] The source anomaly detection module uses the dynamic traceability trajectory of pollutant flow to extract data on the change of pollutant concentration over time, compares the rate of change of pollutant concentration with the pollution diffusion rate in the monitoring interval, identifies abnormal changes in pollutants, and obtains abnormal records of pollutant sources;

[0148] The wastewater risk assessment module calls the abnormal records of pollutant sources, detects the water flow inertia and flow direction gradient of wastewater used for environmental monitoring, calculates the probability value of the pollution diffusion path, determines the pollutant diffusion risk, and obtains the wastewater pollution diffusion assessment results.

[0149] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A wastewater detection method for environmental monitoring based on the Internet of Things, characterized in that: The following steps are involved: S1: Collect wastewater pollutant concentration and water flow velocity data during environmental monitoring, calculate the pollutant concentration change rate, and determine the pollutant diffusion state based on the water flow velocity trend to obtain the pollutant spatial diffusion state characteristics; S2: Based on the spatial diffusion state characteristics of the pollutants, the pollutant component characteristic data of the IoT water monitoring equipment is used to identify the changes in the concentration gradient direction of the pollutant components, and the pollutant concentration change rate is matched with the water flow direction to obtain the cross-regional correlation identification results of the pollutants; S3: Based on the cross-regional association identification results of the pollutants, the offset trajectory of the wastewater vortex center is identified, spatial position matching is performed according to the flow path of the pollutants in the wastewater, and the source of the pollutants flowing into the water body is traced back to generate a dynamic traceability trajectory of the pollutant flow direction; S4: Based on the dynamic traceability trajectory of the pollutant flow, extract the data of the pollutant concentration change over time series, compare the pollutant concentration change rate with the normal pollution diffusion rate data, record the abnormal change interval of the pollutant concentration, identify the abnormal situation of the pollutant source, and obtain the abnormal record of the pollutant source; The steps for obtaining the dynamic traceability trajectory of the pollutant flow are specifically as follows: S311: Analyzing the change trend of water flow velocity at differentiated watershed nodes based on the cross-regional association identification results of the pollutants, calculating the water flow velocity change rate of multiple watershed nodes, and screening watershed nodes with fluctuating water flow velocities to obtain water flow velocity change information for the watershed nodes; S312: Analyze the position offset trajectory of the wastewater vortex center based on the water flow velocity change information of the watershed node, identify the vortex center coordinates at the differentiated time nodes, and use the formula: ； Calculate the swirl center deviation rate value, analyze the change trend of the swirl center deviation rate, and obtain the wastewater swirl center deviation trajectory data; in, represents the swirl center deviation rate, 、 Representative The coordinates of the swirl center of the position, 、 Representative The coordinates of the swirl center of the position, Representative The time node of the location, Representative The time node of the location, is the total number of monitoring points; S313: Using the wastewater vortex center offset trajectory data, spatial position matching is performed according to the pollutant flow path, the source area of the pollutant flowing into the water body is identified, and a dynamic traceability trajectory of the pollutant flow direction is obtained.

2. The method for detecting wastewater for environmental monitoring based on the Internet of Things according to claim 1, wherein: The spatial diffusion state characteristics of the pollutants include the concentration change rate, water flow acceleration trend, and spatial diffusion pattern. The cross-regional association identification results of pollutants include the component feature similarity ratio, concentration gradient direction change, and water flow direction matching information. The dynamic traceability trajectory of the pollutant flow direction includes the offset of the wastewater vortex center, the pollutant flow path, and the source reverse tracking results. The abnormal record of the pollutant source includes the concentration change rate comparison result, the abnormal change interval, and the source abnormality.

3. The method for detecting wastewater for environmental monitoring based on the Internet of Things according to claim 1, wherein: The steps for obtaining the spatial diffusion state characteristics of the pollutants are specifically as follows: S111: Collect wastewater pollutant concentration and water flow velocity data during environmental monitoring, extract the initial concentration, flow velocity data and component characteristics of pollutants, identify the diffusion of pollutants transported by water flow, and obtain the basic diffusion rate of pollutants; S112: Based on the basic diffusion rate of the pollutants and in combination with the water velocity data, identifying the change of the pollutant concentration under the influence of the differentiated water flow acceleration, calculating the concentration change rate of the pollutants under the influence of the water flow acceleration, analyzing the change trend of the pollutant concentration during the water flow transport process, and obtaining the pollutant concentration change trend; S113: By combining the pollutant concentration change trend with the water flow velocity data, the pollutant diffusion state is judged, the concentration change characteristics within the pollutant diffusion area are extracted, and the diffusion range of the pollutants under the differentiated water flow velocity and acceleration changes is analyzed to obtain the pollutant spatial diffusion state characteristics.

4. The method for detecting wastewater for environmental monitoring based on the Internet of Things according to claim 3, wherein: The steps for obtaining the cross-regional correlation identification results of pollutants are specifically as follows: S211: Based on the spatial diffusion state characteristics of the pollutants, the directional change trend of the pollutant concentration gradient is analyzed, the pollutant concentration change rate is calculated, and the matching degree between the pollutant concentration change rate and the water flow direction is evaluated in combination with the water flow direction data, using the formula: ； Calculate the pollutant concentration gradient change matching value, and classify the differentiated pollutant concentration gradient direction changes to obtain the pollutant concentration gradient matching data; in, Represents the matching value of the pollutant concentration gradient change, Representative Pollutant concentration at the location, Representative Pollutant concentration at the location, Representative The spatial distance of the location, Represents the water flow in The flow rate at the location, is the total number of monitoring points; S212: Analyze the matching between the pollutant concentration change rate and the water flow direction through the pollutant concentration gradient matching data, determine whether there is any abnormal cross-change in the pollutant concentration change, screen the cross-regional correlation characteristics of pollutants, and obtain the cross-regional correlation identification results of pollutants.

5. The method for detecting wastewater for environmental monitoring based on the Internet of Things according to claim 1, wherein: The method further comprises step S5: S5: Using the abnormal records of pollutant sources, the flow inertia and flow gradient of the environmental monitoring wastewater are detected, and the probability of pollution diffusion path is deduced to determine the continued diffusion risk of pollutants, analyze the distribution range of pollutant diffusion areas, and obtain the wastewater pollution diffusion assessment results; The wastewater pollution diffusion assessment results include water flow inertia analysis results, flow direction gradient detection information, continuous diffusion risk judgment results, and regional distribution range analysis results.

6. The method for detecting wastewater for environmental monitoring based on the Internet of Things according to claim 5, characterized in that: The steps for obtaining the wastewater pollution diffusion assessment results are specifically as follows: S511: Detecting the water flow inertia and flow direction gradient of the environmental monitoring wastewater based on the abnormal records of the pollutant source, evaluating the influence of the water flow inertia on the diffusion of pollutants, calculating the directional offset of the flow direction gradient, and obtaining the pollutant diffusion directional offset; S512: Analyzing the mobility of the pollution diffusion path using the pollutant diffusion direction offset, and calculating the probability value of the pollution diffusion path by combining the water flow inertia effect and flow gradient characteristics of the differentiated path, thereby obtaining pollution diffusion path probability data; S513: Based on the pollution diffusion path probability data, determine the continued diffusion risk of pollutants, analyze the coverage of pollutant diffusion, and identify the pollution diffusion impact of risk areas to obtain wastewater pollution diffusion assessment results.

7. The wastewater detection system for environmental monitoring based on the Internet of Things is characterized by: The method for detecting wastewater for environmental monitoring based on the Internet of Things according to any one of claims 1 to 6, wherein the system comprises: The pollutant monitoring module obtains the pollutant concentration, water flow velocity and pollutant component characteristic data of wastewater for environmental monitoring, calculates the pollutant concentration change rate, extracts the water flow acceleration trend, calls the water flow acceleration trend and the pollutant concentration change rate, analyzes the time series characteristics of the pollutant diffusion state, and obtains the spatial diffusion state characteristics of the pollutants; The water flow direction identification module extracts the pollutant component characteristic data from the IoT water body online monitoring equipment based on the spatial diffusion state characteristics of the pollutants, analyzes the matching between the pollutant concentration change rate and the water flow direction, and obtains the cross-regional correlation identification results of pollutants; The deviation trajectory identification module calls the cross-regional correlation identification results of the pollutants, analyzes the changing trend of water flow velocity at the intersection of differentiated watersheds, identifies the deviation trajectory of the core point of the wastewater vortex, evaluates the spatial matching degree of the pollutant flow path, and obtains the dynamic traceability trajectory of the pollutant flow direction; The source anomaly detection module calls the dynamic traceability trajectory of the pollutant flow, extracts the data of the pollutant concentration change over time, compares the pollutant concentration change rate with the pollution diffusion rate in the monitoring interval, identifies the abnormal change information of the pollutant, and obtains the abnormal record of the pollutant source; The wastewater risk assessment module calls the abnormal records of the pollutant sources, detects the water flow inertia and flow direction gradient of the wastewater used for environmental monitoring, calculates the probability value of the pollution diffusion path, determines the pollutant diffusion risk, and obtains the wastewater pollution diffusion assessment result.

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