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

Abstract

The invention relates to the technical field of wastewater monitoring, in particular to a wastewater detection method and system for environmental monitoring based on the Internet of Things, and the method comprises the following steps: collecting wastewater pollutant concentration and water velocity data in an environmental monitoring process, calculating a pollutant concentration change rate, and judging a pollutant diffusion state in combination with a water velocity trend. And the spatial diffusion state characteristics of the pollutants are obtained. According to the invention, through real-time monitoring and data analysis of the Internet of Things equipment, determination of pollution sources and pollution diffusion trends is accelerated, especially calculation and comparison of pollutant concentration change rates in multiple areas are carried out, cross identification of pollutant sources is enhanced, so that positioning of the pollution sources is more accurate, and through dynamic tracking and reverse traceability technologies, real-time tracking of the pollution sources is realized. According to the method, the flow direction of the pollutant can be accurately determined, and the accuracy and timeliness of environmental risk assessment are further enhanced by analyzing the time sequence change of the pollutant concentration and recording and identifying the abnormal change interval of the pollutant concentration.

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 technology includes the application of technology to analyze and control the quality of wastewater generated in production and life processes. The core content is to evaluate the potential risks to the environment and public health by monitoring the chemical substances, biological components and physical properties in wastewater. The technical field involves a variety of monitoring methods, including spectral analysis, biochemical detection, heavy metal detection and organic pollutant analysis. Technical cooperation has formed a systematic wastewater monitoring network to track the effect of wastewater treatment in real time and ensure that the water quality meets regulatory requirements.

[0003] Among them, the wastewater detection method for environmental monitoring refers to a series of wastewater quality detection technologies designed for environmental monitoring purposes. The technical matters targeted by the patent subject include the optimization of sampling methods, the improvement of detection accuracy and the acceleration of detection speed. The specific technical means used include sample pretreatment technology, standardized detection process and quantitative analysis technology of specific pollutants. The technical means together constitute the core content of the patent and provide 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 to rapid changes in pollution events in a timely manner. Traditional technologies are inefficient in identifying pollution sources in multiple regions, and it is 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 scheme, a wastewater detection method for environmental monitoring based on the Internet of Things, comprising the following steps: S1: Collect wastewater pollutant concentration and water flow velocity data during environmental monitoring, calculate the pollutant concentration change rate, determine the pollutant diffusion state based on the water flow velocity trend, and 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 change in the direction of the pollutant component concentration gradient, match the pollutant concentration change rate with the water flow direction, and obtain the cross-regional correlation identification result of the pollutants; S3: According to the cross-regional association identification result of the pollutants, the offset trajectory of the wastewater vortex center is identified, the spatial position is matched according to the flow path of the pollutants in the wastewater, the source of the pollutants flowing into the water body is traced back, and a dynamic traceability trajectory of the pollutant flow direction is generated; S4: Based on the dynamic traceability trajectory of the pollutant flow, extract the data of pollutant concentration changes with time series, 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 sources.

[0007] As a further solution of the present invention, the spatial diffusion state characteristics of the pollutants include the concentration change rate, the water flow acceleration trend, and the spatial diffusion pattern. The cross-regional association identification results of the pollutants include the component characteristic similarity ratio, the concentration gradient direction change, and the water flow direction matching information. The dynamic traceability trajectory of the pollutant flow direction includes the wastewater vortex center offset, the pollutant flow path, and the source reverse tracking results. The pollutant source abnormality record includes the concentration change rate comparison results, the abnormal change interval, and the source abnormality.

[0008] As a further solution of the present invention, the step of acquiring the spatial diffusion state characteristics of the pollutants is specifically as follows: S111: Collect wastewater pollutant concentration and water flow velocity data during environmental monitoring, extract 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, the change of the pollutant concentration under the influence of the differentiated water flow acceleration is identified using the formula: ; 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 change trend of pollutant concentration; in, represents the rate of change of pollutant concentration, Representative The pollutant concentration at a location, Representative The pollutant concentration at a location, Representative The water velocity at the location, Representative The water flow acceleration at the position, is a decimal, is the total number of positions; 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, the diffusion range of the pollutants under the differentiated water flow velocity and acceleration changes is analyzed, and the pollutant spatial diffusion state characteristics are obtained.

[0009] As a further solution of the present invention, the steps for obtaining the pollutant cross-region association identification result 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 The pollutant concentration at a location, Representative The pollutant concentration at a location, represent The spatial distance of the location, Represents water flow 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 an abnormal crossover in the pollutant concentration change, screen the cross-regional correlation characteristics of pollutants, and obtain the cross-regional correlation identification results of pollutants.

[0010] As a further solution of the present invention, the steps for obtaining the dynamic traceability trajectory of the pollutant flow are specifically as follows: S311: Analyze the change trend of water flow velocity at differentiated river basin nodes through the cross-regional association identification result of the pollutants, calculate the change rate of water flow velocity at multiple river basin nodes, and select river basin nodes with fluctuating water flow velocity to obtain water flow velocity change information at river basin nodes; S312: Analyze the position deviation trajectory of the wastewater vortex center according to 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 displacement 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 cyclone center offset trajectory data, spatial position matching is performed according to the pollutant flow path, the source area where the pollutants flow into the water body is identified, and a dynamic traceability trajectory of the pollutant flow direction is obtained.

[0011] As a further solution of the present invention, the steps for obtaining the abnormal records of pollutant sources are specifically as follows: S411: Based on the dynamic traceability trajectory of the pollutant flow direction, extract the data of pollutant concentration changes with time series, compare the difference between the pollutant concentration change rate and the normal pollution diffusion rate data, and obtain the pollutant concentration change deviation identification result; S412: According to the pollutant concentration change rate deviation identification result, the interval of abnormal pollutant concentration rate change is screened, the corresponding time period is recorded, and the difference between the pollutant concentration rate change and the normal diffusion rate is analyzed, using the formula: ; Calculate the pollutant concentration change rate deviation value, and combine the time period information of the abnormal change to obtain the abnormal change interval of the pollutant concentration; in, Represents the deviation value of the pollutant concentration change rate, , represents the pollutant concentration at the beginning and end of the time period, , Represents the corresponding time point, represents the normal pollution diffusion rate; S413: Based on the abnormal change interval of pollutant concentration, analyze the characteristics of pollutant sources, match the changes in pollutant flow direction within the interval, identify the sources of abnormal pollutants, and obtain abnormal records of pollutant sources.

[0012] As a further solution of the present invention, the method further comprises step S5: S5: Through the abnormal records of pollutant sources, the water flow inertia and flow direction gradient of the wastewater for environmental monitoring are detected, the probability of pollution diffusion path is deduced, the risk of continuous diffusion of pollutants is determined, the distribution range of pollutant diffusion areas is analyzed, and the wastewater pollution diffusion assessment results are obtained; 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.

[0013] As a further solution of the present invention, the steps for obtaining the wastewater pollution diffusion assessment result are specifically as follows: S511: Detecting the water flow inertia and flow direction gradient of the environmental monitoring wastewater through the abnormal records of the pollutant sources, evaluating the influence of the water flow inertia on the diffusion of pollutants, calculating the direction offset of the flow direction gradient, and obtaining the direction offset of the pollutant diffusion; S512: The pollutant diffusion direction offset is used to analyze the mobility level of the pollution diffusion path, and the water flow inertia influence and flow gradient characteristics of the differentiated path are combined to adopt the formula: ; Calculate the probability value of the pollution diffusion path and obtain the probability data of the pollution diffusion path; in, represents the probability value of the pollution diffusion path, represents the flow trend strength of the pollution diffusion path, Represents the water flow inertia influence weight of the path, Represents the directional offset of the flow gradient, represents the average deviation angle; S513: Based on the pollution diffusion path probability data, 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 wastewater pollution diffusion assessment results.

[0014] 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: The pollutant monitoring module obtains the pollutant concentration, water flow velocity and pollutant component characteristic data of the 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 pollutant spatial diffusion state characteristics; The water flow direction identification module extracts the pollutant component characteristic data of 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 change trend of the water flow velocity at the intersection of the 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 changing with the time series, 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 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 result.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: 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 can enhance the cross-identification of pollutant sources and make the location 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 assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 A flow chart of obtaining the spatial diffusion state characteristics of pollutants in the present invention; Figure 3 A flowchart of obtaining the cross-regional association identification results of pollutants in the present invention; Figure 4 A flow chart of obtaining the dynamic traceability trajectory of pollutant flow in the present invention; Figure 5 A flowchart of obtaining abnormal records of pollutant sources in the present invention; Figure 6 The present invention is a flowchart for obtaining wastewater pollution diffusion assessment results. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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.

[0018] 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 indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0019] 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: S1: Collect wastewater pollutant concentration and water flow velocity data during environmental monitoring, calculate the pollutant concentration change rate, determine the pollutant diffusion state based on the water flow velocity trend, and obtain the pollutant spatial diffusion state characteristics; S2: Based on the spatial diffusion state characteristics of pollutants, the pollutant component characteristic data of the IoT water monitoring equipment is used to identify the change in the direction of the pollutant component concentration gradient, match the pollutant concentration change rate with the water flow direction, and obtain the cross-regional correlation identification results of pollutants; S3: Based on the cross-regional correlation identification results of pollutants, analyze the change trend of water flow velocity at differentiated basin nodes, identify the offset trajectory of the wastewater vortex center, match the spatial position according to the pollutant flow path of the wastewater, reversely trace the source of pollutants flowing into the water body, and generate a dynamic traceability trajectory of pollutant flow direction; S4: Based on the dynamic traceability of pollutant flow, extract the data of pollutant concentration changes over time series, compare the pollutant concentration change rate with the normal pollution diffusion rate data, record the abnormal change interval of pollutant concentration, identify the abnormal situation of pollutant source, and obtain the abnormal record of pollutant source; S5: Through the abnormal records of pollutant sources, the water flow inertia and flow direction gradient of environmental monitoring wastewater are detected, the probability of pollution diffusion path is deduced, the risk of continuous diffusion of pollutants is determined, the distribution range of pollutant diffusion areas is analyzed, and the wastewater pollution diffusion assessment results are obtained; 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 component characteristic similarity ratio, concentration gradient direction change, and water flow direction matching information. The dynamic traceability trajectory of pollutant flow direction 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.

[0020] See also Figure 2 , the specific steps for obtaining the spatial diffusion state characteristics of pollutants are: S111: Collect wastewater pollutant concentration and water flow velocity data during environmental monitoring, extract 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; When setting up monitoring points, it is necessary to select a suitable monitoring area and set up a sampling point at a fixed distance in 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), 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 0.8mg / 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 0.85m / s, the pollutant component characteristics can be quantitatively analyzed by gas chromatography-mass spectrometry. After the data of each monitoring point are sorted out, 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. 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: (6.1-5.2) / 10=0.09; If the TOC at the third monitoring point is 7.0 mg / L, the change rate is calculated as: (7.0-6.1) / 10=0.09; 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; Get the basic diffusion rate of pollutants.

[0021] S112: Based on the basic diffusion rate of pollutants, combined with water velocity data, identify the changes in pollutant concentration under the influence of differentiated water flow acceleration, using the formula: ; 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 change trend of pollutant concentration; in, represents the rate of change of pollutant concentration, Representative The pollutant concentration at a location, Representative The pollutant concentration at a location, Representative The water velocity at the location, Representative The water flow acceleration at the position, is a decimal, is the total number of positions; Parameter meaning and calculation process: parameter Representative The pollutant concentration at the monitoring point is in mg / L. The data is obtained through on-site monitoring. If the concentrations at monitoring points 1, 2, and 3 are mg / L, mg / L, mg / L; Water flow rate Representative The water flow velocity at the monitoring point, in m / s, is measured using an ultrasonic flow meter. m / s, m / s, m / s; Water flow acceleration Representative The rate of change of water flow velocity at the monitoring point is calculated by dividing the change of flow velocity at adjacent monitoring points by the time interval. The time interval is set to 5s, and the acceleration is calculated as follows: ; ; Calculate the rate of change of pollutant concentration: ; ; ; ; Substitute the above data into the formula for calculation: ; The results show that the pollutant concentration change rate is 0.890. The higher the value, the greater the change in pollutant concentration affected by water flow. It is positively correlated with water flow acceleration and concentration change rate. This result is used to judge the subsequent pollutant diffusion state.

[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, the diffusion range of the pollutants under the differential water flow velocity and acceleration changes is analyzed, and the spatial diffusion state characteristics of the pollutants are obtained; 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: ; The pollutant concentration change rate calculated in the previous step and water flow speed Substitute into the calculation: ; This value is much larger than the set threshold, so the pollutant diffusion rate is faster. The concentration change characteristics in the pollutant diffusion area are screened, and the diffusion range of pollutants under different water flow velocities and acceleration changes is further analyzed to obtain the spatial diffusion state characteristics of pollutants.

[0023] See also Figure 3 , the specific steps for obtaining the cross-regional association identification results of pollutants are: S211: Based on the spatial diffusion characteristics of pollutants, analyze the directional change trend of pollutant concentration gradient, calculate the pollutant concentration change rate, and combine the water flow direction data to evaluate the matching degree between the pollutant concentration change rate and the water flow direction. The formula is: ; 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 The pollutant concentration at a location, Representative The pollutant concentration at a location, represent The spatial distance of the location, Represents water flow The flow rate at the location, is the total number of monitoring points; The formula can quantify the diffusion trend of pollutants in water bodies by combining the rate of change of concentration gradient with the water flow velocity, and numerically characterize whether the pollutants diffuse steadily with the water flow or are disturbed 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. Formula parameter value acquisition process: Pollutant concentration ( ): Collected through online water monitoring equipment, such as water quality sensors can measure the real-time concentration of pollutants (unit ); Spatial distance ( ): By setting the distance between monitoring points, according to the water body monitoring point layout plan, all monitoring points are set 100 meters apart; Water flow rate ( ): Through the flow meter measurement, it can be combined with the hydrological station or buoy flow monitoring equipment to set the measured data as follows: , , , , ; The pollutant concentration data collected by the water monitoring equipment is obtained and divided according to the spatial location. For example, multiple monitoring points are set in a certain river section, and each monitoring point obtains the instantaneous concentration value of the pollutant. The concentration of pollutant A measured at 5 monitoring points is , , , , 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: ; All gradient values ​​are calculated in this way to obtain the diffusion trend of pollutants along the water body; Formula calculation example: The concentrations of pollutant A at five monitoring points in the water body are set as follows: ; Distance between adjacent monitoring points , water flow velocity: ; Substituting into the formula: ; ; The results show that the matching value of the pollutant concentration gradient change is 0.00546. The threshold of the concentration gradient change is set in the calculation process of the concentration gradient matching. The threshold is used to determine whether the pollutant diffusion rate is within the normal range. The setting basis is the pollutant diffusion situation 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. The situation below this value is an abnormal pollution source. As the basis for cross-contamination judgment, it is found that the 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 it is necessary to further analyze the source of the pollutant and the pollution characteristics of the associated area.

[0024] 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 an abnormal crossover in the pollutant concentration change, screen the cross-regional correlation characteristics of pollutants, and obtain the cross-regional correlation identification results of pollutants; 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 somewhere (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 is lower than this threshold, it is determined to be an abnormal area. The matching value calculated in the monitoring point 3 area is 0.0018, which is lower than the set threshold. 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.

[0025] See also Figure 4 ,The specific steps for obtaining the dynamic traceability trajectory of pollutant flow are: S311: Analyze the change trend of water flow velocity at differentiated river basin nodes through the cross-regional association identification results of pollutants, calculate the change rate of water flow velocity at multi-watershed nodes, and screen the river basin nodes with fluctuating water flow velocity to obtain the water flow velocity change information of the river basin nodes; 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 velocities 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 90% quantile of all water flow velocity change rate data in the past 365 days. The number means that in 90% of the time, the rate of change of water flow velocity is lower than this value, which is used as the criterion for abnormal fluctuation. The threshold is set to the 90% quantile of the rate of change of water flow velocity. Through the analysis of data throughout the year, the 90% quantile change rate of the basin node is set to 0.3. When the water flow velocity of a 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, the node is classified as an abnormal basin node. According to the water flow change trend of each node, the distribution map of water flow velocity change is drawn, and the distribution range and impact area of ​​the abnormal area are identified to obtain the water flow velocity change information of the basin node.

[0026] S312: According to the water flow velocity change information of the watershed node, the position offset trajectory of the wastewater cyclone center is analyzed, and the coordinates of the cyclone center under the differentiated time nodes are identified, using 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 displacement 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; Parameter meaning and calculation derivation process: The spatial coordinates of the vortex center are collected by a high-precision GPS system. The data at 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: Compute the drift rate for the first time interval: ; Compute the drift rate for the second time interval: ; ; Accumulating all time points, the total offset rate is: ; 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.

[0027] S313: Using the wastewater cyclone center offset trajectory data, spatial position matching is performed according to the pollutant flow path, the source area where the pollutants flow into the water body is identified, and the dynamic traceability trajectory of the pollutant flow direction is obtained; The spatial distribution data of pollutant flow paths are obtained. Based on the time series monitoring data of pollutant concentration, the time interval is set to 5 minutes, and the concentration gradient changes of pollutants at different time points are calculated. The pollutant concentration data are collected through 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 of 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 the pollutants migrate with the water flow direction. If the direction of the pollutant concentration gradient is consistent with the water flow direction, it is determined that the pollutants migrate due to hydrodynamic drive. If the direction of the pollutant concentration gradient is opposite to the water flow direction, it is determined that the pollutants have an external input source. The pollutant concentration gradient threshold is set to the 90% quantile during the pollutant flow period in the basin in the past year. This quantile indicates that the pollutant concentration gradient is lower than this value in 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: (1.2-0.8) / 20=0.02; The results show that the concentration gradient is equal to the threshold value, which means that the pollutant concentration in this area changes dramatically. 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.

[0028] See also Figure 5 , the specific steps for obtaining abnormal records of pollutant sources are: S411: Based on the dynamic traceability trajectory of pollutant flow, extract the data of pollutant concentration changes over time series, compare the difference between the pollutant concentration change rate and the normal pollution diffusion rate data, and obtain the identification result of pollutant concentration change deviation; Extract the data of pollutant concentration changing with time series. This process requires monitoring the concentration value of pollutants at each time point and arranging them in chronological order to form time series data. The pollutant concentrations collected at monitoring points A, B, and C in a river are 10 mg / L, 15 mg / L, and 20 mg / L, respectively, and the collection time interval is 10 minutes. The data can be expressed as , ; To calculate the rate of change of pollutant concentration in each time period, differential calculation can be used, namely: ; ; At this time, the pollutant concentration change rate of two adjacent time periods is obtained, and the normal pollution diffusion rate data is called. This data can be measured by real-time data. In the absence of external pollution source intervention, the normal diffusion rate of pollutants in the river is set to 0.3. Then, the difference between the two is compared to calculate the deviation value of the pollutant concentration change rate. The calculation method is: ; in, is the calculated pollutant concentration change rate, is the normal pollution diffusion rate; The calculation results are: ; ; When the deviation value is higher than 30%, the concentration change is considered abnormal, and the pollutant concentration change deviation identification result is obtained.

[0029] S412: According to the pollutant concentration change rate deviation identification result, the interval with abnormal pollutant concentration rate change is screened, the corresponding time period is recorded, and the difference between the pollutant concentration rate change and the normal diffusion rate is analyzed using the formula: ; Calculate the pollutant concentration change rate deviation value, and combine the time period information of the abnormal change to obtain the abnormal change interval of the pollutant concentration; in, Represents the deviation value of the pollutant concentration change rate, , represents the pollutant concentration at the beginning and end of the time period, , Represents the corresponding time point, represents the normal pollution diffusion rate; The meaning of the parameters are as follows: , Represents the pollutant concentration at the start and end of the time period, in mg / L. The data is obtained through online monitoring equipment at the river section monitoring point. The monitoring equipment is at the time point Recording pollutant concentrations mg / L, at time point Recording pollutant concentrations mg / L; , Represents the corresponding time point, unit min. This data is provided by the automatic water quality monitoring equipment, and the time accuracy can reach the minute level. , ; Represents the normal pollution diffusion rate, which is calculated based on real-time data analysis. In the absence of external pollution source intervention, the diffusion rate is calculated based on the river velocity, turbulent diffusion coefficient and water body self-purification capacity. This calculation is set ; The formula calculation process is as follows: Calculate the rate of change of pollutant concentration during this time period: ; Calculate the deviation value of the pollutant concentration change rate: ; The result shows that the rate of change of pollutant concentration is 0.2 higher than the normal diffusion rate, indicating that the rate of change of pollutant concentration during this period is abnormal and there is external pollution source input. This value can be used to screen the abnormal change range of pollutant concentration.

[0030] S413: Based on the abnormal change interval of pollutant concentration, analyze the characteristics of pollutant sources, match the changes in pollutant flow direction within the interval, identify the abnormal pollutant sources, and obtain abnormal records of pollutant sources; Analyze the characteristics of pollutant sources, extract pollutant flow data within the interval, and determine the direction of pollutant sources by combining water flow velocity and flow direction information. If the interval with abnormal increase in pollutant concentration is between monitoring points AB, and the water flow direction is from A to B, then the source of pollutants should be located in the upstream area of ​​A. Further match the pollutant emission records within the interval to determine the estimated source of pollution and extract pollutant emission data for the area. For example, there is an industrial discharge port D 5 km upstream of A. The discharge port discharges 100L of industrial wastewater in the time period (T1-T2), with a pollutant concentration of 50mg / L and a total pollutant emission of mg, calculate the impact of this discharge on river water quality, set the river flow to 500, and within this time period, the increase in river pollutant concentration caused by the discharge of pollutants is: ; Compared with the river monitoring data, the pollutant concentration increment in this interval is: ; The contribution of this discharge outlet to the change in pollutant concentration is: ; If the contribution rate is lower than 50%, it means that the pollution source is not the main source. If it exceeds 50%, D is determined to be the main pollution source. Further analysis of D's emissions over a longer time scale is conducted, and combined with its emission flow and pollutant composition, it is determined whether there are excessive emissions and abnormal records of pollutant sources are obtained.

[0031] See also Figure 6, the specific steps for obtaining the wastewater pollution diffusion assessment results are: S511: Detect the water flow inertia and flow direction gradient of wastewater for environmental monitoring through abnormal records of pollutant sources, evaluate the influence of water flow inertia on pollutant diffusion, calculate the direction offset of flow direction gradient, and obtain the direction offset of pollutant diffusion; It is necessary to measure the flow characteristics of wastewater for environmental monitoring, including the changes in water flow inertia and flow gradient. In the actual monitoring process, flow velocity sensors can be arranged at multiple points at the pollutant discharge port and downstream. By monitoring the changes in flow velocity over time, the inertia strength of the water flow can be calculated. The 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: ; The 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: ; Thus, the changing trend of the wastewater flow direction can be obtained. In this process, it is necessary to set a threshold value of 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. The flow gradient data monitored in a certain area are -0.01, -0.015, -0.005, 0.002, and -0.012, respectively. The mean value is calculated as: ; The standard deviation is calculated as: ; Therefore, the flow gradient change rate threshold can be ; It indicates that there is a risk of pollution diffusion in the area beyond this value. At the same time, the offset of the pollutant diffusion direction can be determined by further 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 changes 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: ; Get the pollutant diffusion direction offset.

[0032] S512: The pollutant diffusion direction offset is used to analyze the mobility level of the pollution diffusion path. Combined with the water flow inertia influence and flow gradient characteristics of the differentiated path, the formula is used: ; Calculate the probability value of the pollution diffusion path and obtain the probability data of the pollution diffusion path; in, represents the probability value of the pollution diffusion path, represents the flow trend strength of the pollution diffusion path, Represents the water flow inertia influence weight of the path, Represents the directional offset of the flow gradient, represents the average deviation angle; The parameters are obtained as follows: Flow trend strength The velocity change rate in the pollution diffusion area is calculated by recording the velocity at different time points through the hydrodynamic monitoring equipment, and calculating the average change in time. In a monitoring area, the water velocity at measuring point A is set to increase from 0.5m / s to 1.0m / s, and the measurement time interval is 10 seconds. The flow trend intensity is calculated as follows: ; Water flow inertia influence weight Based on the normalized calculation of water flow inertia of each diffusion path in the area, the calculation method is: ; in, represents the inertia of water flow on a certain path, represents the sum of water flow inertia on all diffusion paths; In a monitoring area, the water flow inertia of four diffusion paths was recorded as 0.3, 0.4, 0.5 and 0.6 respectively. For a specific path, the water flow inertia influence weight is calculated as follows: ; Flow gradient direction offset Calculated by the flow velocity change rate between different monitoring points, the flow velocity of the upstream monitoring point B is set to 1.2m / s, the flow velocity of the downstream monitoring point C is set to 0.9m / s, and the distance between the two points is 50m, then the calculation is as follows: ; Average deviation angle According to the change of flow gradient vector, the average deviation angle is calculated by the angle difference of flow direction at different monitoring points. If the flow directions of five monitoring points in a certain area are 5, 15, 10, 12, and 18 respectively, the average deviation angle is: ; Substitute the above calculation results into the formula to calculate the probability value of the pollution diffusion path: ; The results show that under the monitored flow field environment, the probability value of pollutants diffusing along this path is 3.475. Compared with the remaining paths in the area, if the diffusion probability values ​​of the remaining paths are all lower than 3.0, it indicates that this path has become the main pollution diffusion channel. It is necessary to focus on the pollution diffusion situation in this area in subsequent analysis, and further determine the pollution diffusion range in combination with the pollutant concentration monitoring results.

[0033] 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 impact of pollution diffusion in risk areas to obtain wastewater pollution diffusion assessment results; 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: ; 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: ; 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 benchmark value to determine whether the pollutant has reached the high-risk diffusion area. The pollutant concentration benchmark value can refer to the local environmental standards. The standard limit of wastewater pollutants in a certain place is set to 0.3 mg / L. If the pollutant concentration exceeds 1.5 times of this value, that is: ; The area can be considered as a high-risk pollution area, and the high-risk diffusion area is 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.

[0034] The wastewater detection system for environmental monitoring based on the Internet of Things is used to execute the above-mentioned wastewater detection method for environmental monitoring based on the Internet of Things, and the system includes: The pollutant monitoring module obtains the pollutant concentration, water flow velocity and pollutant component characteristic data of the 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 pollutant spatial diffusion state characteristics; The water flow direction identification module extracts the pollutant component characteristic data of 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 pollutants, analyzes the change 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 pollutant flow, extracts the data of pollutant concentration changes over time, compares the pollutant concentration change rate with the pollution diffusion rate in the monitoring interval, identifies the abnormal change information of pollutants, and obtains the abnormal records of pollutant sources; The wastewater risk assessment module calls up abnormal records of pollutant sources, detects the flow inertia and flow gradient of wastewater used for environmental monitoring, calculates the probability value of the pollution diffusion path, determines the risk of pollutant diffusion, and obtains wastewater pollution diffusion assessment results.

[0035] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope 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, determine the pollutant diffusion state based on the water flow velocity trend, and 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 change in the direction of the pollutant component concentration gradient, match the pollutant concentration change rate with the water flow direction, and obtain the cross-regional correlation identification result of the pollutants; S3: According to the cross-regional association identification result of the pollutants, the offset trajectory of the wastewater vortex center is identified, the spatial position is matched according to the flow path of the pollutants in the wastewater, the source of the pollutants flowing into the water body is traced back, and a dynamic traceability trajectory of the pollutant flow direction is generated; S4: Based on the dynamic traceability trajectory of the pollutant flow, extract the data of pollutant concentration changes with time series, 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 sources.

2. The method for detecting wastewater for environmental monitoring based on the Internet of Things according to claim 1, characterized in that: 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 component characteristic similarity ratios, concentration gradient direction changes, and water flow direction matching information. The dynamic traceability trajectory of pollutant flow direction 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 concentration change rate comparison results, abnormal change intervals, and source abnormalities.

3. The method for detecting wastewater for environmental monitoring based on the Internet of Things according to claim 1, characterized in that: 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 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, the change of the pollutant concentration under the influence of the differentiated water flow acceleration is identified using the formula: ； 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 change trend of pollutant concentration; in, represents the rate of change of pollutant concentration, Representative The pollutant concentration at a location, Representative The pollutant concentration at a location, Representative The water velocity at the location, Representative The water flow acceleration at the position, is a decimal, is the total number of positions; 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, the diffusion range of the pollutants under the differentiated water flow velocity and acceleration changes is analyzed, and the pollutant spatial diffusion state characteristics are obtained.

4. The method for detecting wastewater for environmental monitoring based on the Internet of Things according to claim 3 is characterized in that: The steps for obtaining the pollutant cross-region association identification results 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 The pollutant concentration at a location, Representative The pollutant concentration at a location, represent The spatial distance of the location, Represents water flow 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 an abnormal crossover 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 4 is characterized in that: The steps for obtaining the dynamic traceability trajectory of the pollutant flow are specifically as follows: S311: Analyze the change trend of water flow velocity at differentiated river basin nodes through the cross-regional association identification result of the pollutants, calculate the change rate of water flow velocity at multiple river basin nodes, and select river basin nodes with fluctuating water flow velocity to obtain water flow velocity change information at river basin nodes; S312: Analyze the position deviation trajectory of the wastewater vortex center according to 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 displacement 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 cyclone center offset trajectory data, spatial position matching is performed according to the pollutant flow path, the source area where the pollutants flow into the water body is identified, and a dynamic traceability trajectory of the pollutant flow direction is obtained.

6. The method for detecting wastewater for environmental monitoring based on the Internet of Things according to claim 5, characterized in that: The specific steps for obtaining the abnormal records of pollutant sources are: S411: Based on the dynamic traceability trajectory of the pollutant flow direction, extract the data of pollutant concentration changes with time series, compare the difference between the pollutant concentration change rate and the normal pollution diffusion rate data, and obtain the pollutant concentration change deviation identification result; S412: According to the pollutant concentration change rate deviation identification result, the interval of abnormal pollutant concentration rate change is screened, the corresponding time period is recorded, and the difference between the pollutant concentration rate change and the normal diffusion rate is analyzed, using the formula: ； Calculate the pollutant concentration change rate deviation value, and combine the time period information of the abnormal change to obtain the abnormal change interval of the pollutant concentration; in, Represents the deviation value of the pollutant concentration change rate, , represents the pollutant concentration at the beginning and end of the time period, , Represents the corresponding time point, represents the normal pollution diffusion rate; S413: Based on the abnormal change interval of pollutant concentration, analyze the characteristics of pollutant sources, match the changes in pollutant flow direction within the interval, identify the sources of abnormal pollutants, and obtain abnormal records of pollutant sources.

7. The method for detecting wastewater for environmental monitoring based on the Internet of Things according to claim 1, characterized in that: The method further comprises step S5: S5: Through the abnormal records of pollutant sources, the water flow inertia and flow direction gradient of the wastewater for environmental monitoring are detected, the probability of pollution diffusion path is deduced, the risk of continuous diffusion of pollutants is determined, the distribution range of pollutant diffusion areas is analyzed, and the wastewater pollution diffusion assessment results are obtained; 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.

8. The method for detecting wastewater for environmental monitoring based on the Internet of Things according to claim 7, 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 through the abnormal records of the pollutant sources, evaluating the influence of the water flow inertia on the diffusion of pollutants, calculating the direction offset of the flow direction gradient, and obtaining the direction offset of the pollutant diffusion; S512: The pollutant diffusion direction offset is used to analyze the mobility level of the pollution diffusion path, and the water flow inertia influence and flow gradient characteristics of the differentiated path are combined to adopt the formula: ； Calculate the probability value of the pollution diffusion path and obtain the probability data of the pollution diffusion path; in, represents the probability value of the pollution diffusion path, represents the flow trend strength of the pollution diffusion path, Represents the water flow inertia influence weight of the path, represents the directional offset of the flow gradient, represents the average deviation angle; S513: Based on the pollution diffusion path probability data, 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 wastewater pollution diffusion assessment results.

9. The wastewater detection system for environmental monitoring based on the Internet of Things is characterized by: According to any one of claims 1 to 8, the method for detecting wastewater for environmental monitoring based on the Internet of Things comprises: The pollutant monitoring module obtains the pollutant concentration, water flow velocity and pollutant component characteristic data of the 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 pollutant spatial diffusion state characteristics; The water flow direction identification module extracts the pollutant component characteristic data of 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 change trend of the water flow velocity at the intersection of the 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 tracing 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 changing with the time series, 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 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 result.

Images