Environmental sewage flow detection system

Abstract

The invention relates to the technical field of sewage detection, and discloses an environmental sewage flow detection system, and the system comprises sewage outlet flow detection units which are disposed at sewage outlets along the bank of a river, and are used for detecting the sewage flow and generating flow time sequence data; the sewage draining exit water quality detection units are deployed at sewage draining exits along the bank of the river and are used for detecting water quality parameters of sewage discharged from the sewage draining exits; according to the invention, the sewage draining exit flow detection unit, the sewage draining exit water quality detection unit and the river channel water quality monitoring unit are arranged and are in communication connection with the intelligent analysis platform; dynamic flow normal interval calculation of the flow analysis module, water quality abnormity judgment of the river water quality analysis module, pollution source space-time correlation analysis of the collaborative traceability module and early warning signal execution of the early warning module are achieved, and the effect of improving sewage flow detection accuracy and system reliability is achieved; and through the anti-blocking and self-cleaning design, blocking interference is reduced, and long-term stable operation is ensured.

Description

Technical Field

[0001] This application relates to the field of wastewater detection technology, and in particular to an environmental wastewater flow detection system. Background Technology

[0002] With the rapid advancement of industrialization and urbanization, water pollution has become an increasingly prominent problem. The disorderly discharge of industrial wastewater and domestic sewage is one of the main reasons for the deterioration of river water quality. In order to effectively control sewage discharge and ensure water environment safety, real-time monitoring of sewage outlet flow and water quality and anomaly tracing have become key links in environmental governance.

[0003] In existing technologies, the determination of normal flow ranges in wastewater flow monitoring is mostly based on fixed thresholds, without considering the historical discharge patterns and periodic fluctuations of different discharge outlets. This can easily lead to misjudgments or missed judgments, especially for industrial discharge outlets with seasonal production characteristics, where fixed thresholds are difficult to adapt to their flow variation characteristics. In terms of water quality monitoring, existing systems often rely on single detection data to determine river water quality anomalies, lacking a mechanism for multiple continuous verifications. This makes them susceptible to instantaneous interference (such as rainstorm erosion or sudden small-scale pollutant leakage) and misjudgment of water quality anomalies. At the same time, when river water quality is abnormal, the pollution source tracing process is inefficient: either the suspected source is simply determined based on the discharge outlet's flow exceeding the standard, ignoring the spatiotemporal correlation between flow and water quality parameters; or when the flow is normal, there is a lack of scientific water quality parameter comparison and analysis methods, making it difficult to quickly locate hidden discharge sources such as "low flow and high concentration". Summary of the Invention

[0004] In order to overcome the problems of unreasonable determination of normal flow range, easy interference in the judgment of water quality anomalies and low efficiency of pollution source tracing in the existing technology, this application provides an environmental sewage flow detection system.

[0005] In a first aspect, this application provides an environmental wastewater flow detection system, which adopts the following technical solution: An environmental wastewater flow detection system, the system comprising: The sewage outlet flow detection unit is deployed at various sewage outlets along the river to detect sewage flow and generate flow time series data; The sewage outlet water quality monitoring unit is deployed at each sewage outlet along the riverbank to monitor the water quality parameters of the sewage discharged from the outlet. River water quality monitoring units are deployed in the river downstream of the sewage outlet to detect river water quality parameters; The intelligent analysis platform, which is communicatively connected to the flow detection unit, the sewage outlet water quality detection unit, and the water quality monitoring unit, includes: The flow analysis module dynamically calculates the normal flow range of each sewage outlet based on historical data to determine whether the sewage discharge flow of each sewage outlet is normal. The river water quality analysis module analyzes the detected river water quality parameters to determine whether the river water quality is abnormal. The collaborative source tracing module prioritizes analyzing the spatiotemporal correlation between sewage outlets exceeding flow limits and water quality parameters when river water quality is abnormal. The early warning module is used to execute the early warning signals generated during the operation of each module.

[0006] By adopting the above technical solutions, an environmental sewage flow detection system was constructed, which includes a sewage outlet flow detection unit, a sewage outlet water quality detection unit, a river water quality monitoring unit, and an intelligent analysis platform. This system enables the detection of sewage outlet flow, water quality, and river water quality. Through the various modules of the intelligent analysis platform, the system completes the judgment of flow normality, analysis of abnormal river water quality, collaborative source tracing, and early warning, thereby comprehensively improving the sewage monitoring and pollution source tracing capabilities.

[0007] Optionally, the process of determining whether the sewage discharge volume of each discharge outlet is normal includes: The flow analysis module collects historical flow time-series data from each sewage outlet; Within the preset sliding time window Calculate the average sewage flow rate of each sewage outlet. and standard deviation ; Through formula The normal flow range of each sewage outlet was obtained through analysis and calculation. ; Among them, the The sensitivity coefficient is adjustable. Wastewater flow data from each discharge outlet The normal flow rate of the sewage outlet Perform a comparison; like If so, the sewage discharge from the outlet is considered normal. like If the sewage discharge from the outlet exceeds the standard, an early warning signal for the sewage discharge from the outlet will be generated. like If the discharge volume of the sewage outlet is abnormal, it is necessary to check whether the flow detection unit of the sewage outlet is working properly.

[0008] By adopting the above technical solution, it is clarified that the mean, standard deviation and adjustable sensitivity coefficient are calculated within a sliding time window based on historical flow time series data to determine the normal flow range. By comparing the detected flow with the normal range, it is possible to judge whether the sewage discharge from the discharge outlet is normal (normal, exceeding the standard, abnormal), thus providing a quantitative standard for flow monitoring.

[0009] Optionally, the process of checking whether the sewage outlet flow detection unit is working properly includes: Step 1: The intelligent analysis platform sends a self-test command to the sewage outlet flow detection unit, which includes the requirements for collecting equipment operating status parameters; Step 2: After receiving the self-test command, the sewage outlet flow detection unit performs a self-test on its own core components, generates a self-test report, and feeds it back to the intelligent analysis platform. The self-test report must include the operating parameters of each component and an indicator of whether it is working properly. Step 3: The intelligent analysis platform analyzes the self-inspection report. If all core components are working properly, it further retrieves the historical operating data of the sewage outlet flow detection unit to check if there are any abnormal data records within the preset time period. Step 4: If there are abnormal data records, the intelligent analysis platform controls the sewage outlet flow detection unit to perform a calibration operation. The calibration is carried out by comparing with a standard flow device. By injecting fluid with a known flow rate, the deviation between the measured value of the detection unit and the standard value is recorded. If the deviation is within the allowable range, the detection unit is judged to be working normally. Step 5: If the calibration deviation exceeds the allowable range, or the self-test report shows that the core components are abnormal, the intelligent analysis platform will generate an equipment fault warning signal and notify the maintenance personnel to carry out on-site repairs. Step Six: After the maintenance personnel complete the inspection, they report the inspection results to the intelligent analysis platform. The intelligent analysis platform controls the sewage outlet flow detection unit to restart and perform a flow detection. The detection result is compared with the normal range. If it returns to normal, the verification ends; if it is still abnormal, the above steps are repeated for a second verification.

[0010] By adopting the above technical solution, a complete process is specified, from sending self-test commands, generating self-test reports, analyzing historical data, calibration operations to fault warning and post-maintenance verification. This enables effective verification of whether the sewage outlet flow detection unit is working properly and ensures the reliability of the flow detection data.

[0011] Optionally, the process of determining whether the river water quality is abnormal includes: The river water quality analysis module collects the values ​​of various water quality indicators obtained by the river water quality monitoring unit. The values ​​of each water quality indicator parameter are compared with the corresponding upper and lower limits. If any water quality indicator parameter value is outside the corresponding upper and lower limits, it is preliminarily determined that the water quality indicator is abnormal in the water quality test. If the water quality indicators initially identified as abnormal are tested and verified multiple times, and the proportion of abnormal water quality indicators exceeds the preset proportion threshold in the multiple tests, then the river water quality is confirmed to be abnormal, and the source tracing module is activated. Otherwise, the river water quality is not determined to be abnormal.

[0012] By adopting the above technical solution, a mechanism was established that first compares the river water quality indicators with the allowable upper and lower limits to make a preliminary judgment on anomalies, and then verifies the results through multiple consecutive tests and confirms whether the water quality is abnormal based on the proportion of abnormal results, thereby improving the accuracy of river water quality anomaly judgment.

[0013] Optionally, the collaborative tracing module's operation includes: The collaborative tracing module collects time-series data of sewage discharge flow corresponding to the sewage outlet discharge flow rate generated by the flow analysis module within a preset time interval, and at the same time retrieves the water quality parameter change curves recorded by the river water quality monitoring unit within the same time period. Calculate the time difference between the peak flow time and the time of abrupt change in river water quality parameters for each sewage discharge outlet exceeding the standard. Combined with the distance between the sewage outlet and the water quality monitoring point Through water flow speed Calculate the theoretical pollutant transport time ; like and If the deviation is within the preset fluctuation range, the corresponding sewage outlet that exceeds the standard will be marked as a sewage outlet with high spatiotemporal matching degree. For sewage outlets with high spatiotemporal matching, further analysis is conducted to determine whether their historical sewage quality data contains characteristic pollutants that are abnormal in the river. If matching characteristic pollutants are found, they are sorted by the product of concentration and flow rate of the characteristic pollutants to generate a list of suspected sources and corresponding pollution suspected source warning signals. If there are no sewage outlets exceeding the standards or no sewage outlets with high spatiotemporal matching within the preset time interval, the sewage outlet water quality detection unit will be activated first according to the industry risk level to obtain water quality parameter data of each sewage outlet. Then, the water quality parameter data of each sewage outlet and the river water quality parameter data will be compared and analyzed to determine whether there is a suspected source of pollution.

[0014] By adopting the above technical solution, the collaborative source tracing module can first analyze the spatiotemporal correlation between the excessive sewage outlets and water quality parameters and generate a list of suspected sources in combination with characteristic pollutants when the river water quality is abnormal. If no list is found, the module can detect the water quality of the sewage outlets according to the industry risk level to investigate suspected sources, thereby improving the efficiency and accuracy of pollution source tracing.

[0015] Optionally, in the process of prioritizing the activation of wastewater outlet water quality testing units according to industry risk levels, the priority ranking rule is as follows: Chemicals > Pharmaceuticals > Printing and Dyeing > Food Processing > Domestic Sewage.

[0016] By adopting the above technical solutions, the rules for activating the sewage outlet water quality testing units were clarified according to the industry risk level priority of "chemical > pharmaceutical > printing and dyeing > food processing > domestic sewage", ensuring that sewage outlets of high-risk industries are tested first and optimizing the source tracing testing sequence.

[0017] Optionally, the process of determining whether a suspected source of pollution exists includes: Calculate the cosine similarity between the detected water quality parameters of the sewage outlet and the water quality parameters of the river channel. The formula is: in, The number of water quality indicators obtained from the test, , The value of the i-th water quality indicator parameter for the wastewater discharged from the sewage outlet. For the i-th water quality index parameter value corresponding to the river channel; If there exists a cosine similarity for a certain water quality indicator If the similarity index exceeds the preset threshold and the water quality index in the river is in an abnormal state, the corresponding sewage outlet will be included in the list of suspected pollution sources, and a corresponding pollution source warning signal will be generated. Conversely, if a suspected undetected sewage outlet is identified, an early warning signal for investigating hidden pollution sources is generated, prompting an expansion of the monitoring scope.

[0018] By adopting the above technical solution, a mechanism was established to determine suspected pollution sources by calculating the cosine similarity between sewage outlets and river water quality indicators, combined with abnormal indicators; otherwise, it prompts the investigation of hidden pollution sources, thereby further accurately identifying suspected pollution sources.

[0019] Optionally, the sewage outlet flow detection unit includes an anti-clogging ultrasonic flow meter with a conical guide shroud at its front end and a built-in rotating self-cleaning brush.

[0020] By adopting the above technical solution, the sewage outlet flow detection unit uses an anti-clogging ultrasonic flow meter with a conical guide shroud and a built-in rotating self-cleaning brush, which effectively prevents clogging and ensures the stability and accuracy of flow detection.

[0021] Secondly, this application provides an environmental wastewater flow detection system, which adopts the following technical solution: A computer device includes a processor running a program for an environmental wastewater flow detection system as described in any one of the preceding claims.

[0022] Thirdly, this application provides a storage medium, which adopts the following technical solution: A storage medium storing a program for an environmental wastewater flow detection system as described in any one of the above.

[0023] In summary, this application includes at least one of the following beneficial technical effects: (1) This invention deploys a sewage outlet flow detection unit, a sewage outlet water quality detection unit, and a river water quality monitoring unit, and connects them with an intelligent analysis platform to realize the dynamic flow normal range calculation of the flow analysis module, the water quality anomaly judgment of the river water quality analysis module, the spatiotemporal correlation analysis of pollution sources of the collaborative source tracing module, and the execution of the early warning signal of the early warning module. This method achieves the effect of improving the accuracy of sewage flow detection and the reliability of the system. Through anti-blocking and self-cleaning design, it reduces blockage interference and ensures long-term stable operation. Attached Figure Description

[0024] Figure 1 This is a schematic block diagram of an environmental wastewater flow detection system proposed in this invention. Detailed Implementation

[0025] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0026] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0027] This application discloses an environmental wastewater flow detection system, referring to... Figure 1 The system includes: The sewage outlet flow detection unit is deployed at various sewage outlets along the river to detect sewage flow and generate flow time series data; The sewage outlet water quality monitoring unit is deployed at each sewage outlet along the riverbank to monitor the water quality parameters of the sewage discharged from the outlet. River water quality monitoring units are deployed in the river downstream of the sewage outlet to detect river water quality parameters; The intelligent analysis platform, which is communicatively connected to the flow detection unit, the sewage outlet water quality detection unit, and the water quality monitoring unit, includes: The flow analysis module dynamically calculates the normal flow range of each sewage outlet based on historical data to determine whether the sewage discharge flow of each sewage outlet is normal. The river water quality analysis module analyzes the detected river water quality parameters to determine whether the river water quality is abnormal. The collaborative source tracing module prioritizes analyzing the spatiotemporal correlation between sewage outlets exceeding flow limits and water quality parameters when river water quality is abnormal. The early warning module is used to execute the early warning signals generated during the operation of each module.

[0028] The sewage outlet flow detection unit includes an anti-clogging ultrasonic flow meter with a conical guide shroud at the front end and a built-in rotating self-cleaning brush.

[0029] Through the above technical solution, this embodiment provides an environmental wastewater flow detection system. The system deploys a discharge outlet flow detection unit, a discharge outlet water quality detection unit, and a river water quality monitoring unit, and communicates with an intelligent analysis platform to realize dynamic flow normal range calculation of the flow analysis module, water quality anomaly judgment of the river water quality analysis module, spatiotemporal correlation analysis of pollution sources by the collaborative source tracing module, and execution of early warning signals by the early warning module. This method achieves the effect of improving the accuracy of wastewater flow detection and system reliability. Through anti-clogging and self-cleaning design, it reduces clogging interference and ensures long-term stable operation.

[0030] In one embodiment, the process of determining whether the sewage discharge volume of each discharge outlet is normal includes: The flow analysis module collects historical flow time-series data from each sewage outlet; Within the preset sliding time window Calculate the average sewage flow rate of each sewage outlet. and standard deviation The preset sliding time window The dynamic time interval used to calculate the historical flow mean and standard deviation can be set according to the discharge pattern of the sewage outlet (such as the periodicity of industrial sewage discharge and the diurnal difference of domestic sewage), and is generally 24 hours, 7 days or 30 days. Among them, the average sewage flow rate Through formula Calculated standard deviation It is possible Calculated Preset sliding time window The number of traffic data points within the area can be determined based on a preset sliding time window. The amount of data collected by the internal sewage outlet flow detection unit is obtained. The sewage discharge flow rate at the j-th time point can be obtained in real time by the sewage outlet flow detection unit. Through formula The normal flow range of each sewage outlet was obtained through analysis and calculation. ; Among them, the This is an adjustable sensitivity coefficient, set according to management needs. Commonly used values ​​are 1-3. Wastewater flow data from each discharge outlet The normal flow rate of the sewage outlet The wastewater flow data of each discharge outlet were compared. Data is directly acquired by detection units such as ultrasonic flow meters; like If so, the sewage discharge from the outlet is considered normal. like If the sewage discharge from the outlet exceeds the standard, an early warning signal for the sewage discharge from the outlet will be generated. like If the discharge volume of the sewage outlet is abnormal, it is necessary to check whether the flow detection unit of the sewage outlet is working properly.

[0031] Through the above technical solution, this embodiment provides a method for determining whether the sewage discharge from a sewage outlet is normal. The method collects historical flow time-series data of each sewage outlet through a flow analysis module, calculates the mean and standard deviation of the sewage discharge flow within a preset sliding time window, and obtains the normal flow range through analysis using a formula (the normal range is the product of the mean plus or minus the adjustable sensitivity coefficient and the standard deviation). Then, the real-time detected sewage flow data is compared with the normal range. If it is within the range, it is determined to be normal; if it exceeds the upper limit, it is determined to be out of standard and an early warning is generated; if it is below the lower limit, it is determined to be abnormal and a verification is triggered. This method has the effect of dynamically adapting to flow changes and timely warning of abnormal discharge, reducing the need for manual intervention.

[0032] In one embodiment, the process of checking whether the sewage outlet flow detection unit is working properly includes: Step 1: The intelligent analysis platform sends a self-test command to the sewage outlet flow detection unit, which includes the requirements for collecting equipment operating status parameters; Step 2: After receiving the self-test command, the sewage outlet flow detection unit performs a self-test on its own core components, generates a self-test report, and feeds it back to the intelligent analysis platform. The self-test report must include the operating parameters of each component and an indicator of whether it is working properly. Step 3: The intelligent analysis platform analyzes the self-inspection report. If all core components are working properly, it further retrieves the historical operating data of the sewage outlet flow detection unit to check if there are any abnormal data records within a preset time period. The preset time period can be set according to the common fault cycle of the equipment, and is usually set to 7 days. Step 4: If there are abnormal data records, the intelligent analysis platform controls the sewage outlet flow detection unit to perform a calibration operation. The calibration is carried out by comparing with a standard flow device. By injecting fluid with a known flow rate, the deviation between the measured value of the detection unit and the standard value is recorded. If the deviation is within the allowable range, the detection unit is determined to be working normally. The allowable range can be set according to the accuracy level of the equipment. Step 5: If the calibration deviation exceeds the allowable range, or the self-test report shows that the core components are abnormal, the intelligent analysis platform will generate an equipment fault warning signal and notify the maintenance personnel to carry out on-site repairs. Step Six: After the maintenance personnel complete the inspection, they report the inspection results to the intelligent analysis platform. The intelligent analysis platform controls the sewage outlet flow detection unit to restart and perform a flow detection. The detection result is compared with the normal range. If it returns to normal, the verification ends; if it is still abnormal, the above steps are repeated for a second verification.

[0033] Through the above technical solution, this embodiment provides a method for verifying the working status of a sewage outlet flow detection unit. The method sends a self-test command (requiring the collection of equipment operating status parameters) through an intelligent analysis platform, receives the self-test report of the flow detection unit (including component operating parameters and working status identifiers), analyzes the report, retrieves historical data to check for abnormal records, and, if necessary, calibrates the control unit (by injecting a fluid with a known flow rate and comparing it with a standard value). If the deviation exceeds the limit or the component is abnormal, an equipment fault warning is generated and on-site maintenance is notified. After maintenance, the test and comparison are repeated. If the abnormality is still present, the verification is repeated. This method achieves the effect of automated equipment maintenance and rapid fault diagnosis, improving the reliability of the detection unit and the accuracy of the data.

[0034] In one embodiment, the process of determining whether the river water quality is abnormal includes: The river water quality analysis module collects the values ​​of various water quality indicators obtained by the river water quality monitoring unit. The values ​​of each water quality indicator parameter are compared with the corresponding upper and lower limits. If any water quality indicator parameter value is outside the corresponding upper and lower limits, it is preliminarily determined that the water quality indicator is abnormal in the water quality test. The water quality indicators initially determined to be abnormal are subjected to multiple consecutive tests for verification. If the proportion of abnormal water quality indicators in the multiple consecutive test results exceeds the preset proportion threshold, the river water quality is confirmed to be abnormal, and the source tracing module is activated. Otherwise, the river water quality is not determined to be abnormal. The number of consecutive tests can be set according to the stability of the water quality indicators, with a commonly used value of 3 to 5 times. The preset proportion threshold can be set according to the importance of the indicators, with a commonly used value of 50% to 80%.

[0035] Through the above technical solution, this embodiment provides a method for determining whether river water quality is abnormal. The method collects detection data from river water quality monitoring units through a river water quality analysis module, compares the values ​​of various water quality indicators with the allowable upper and lower limits, and preliminarily determines an abnormality if any indicator exceeds the range. Then, the abnormal indicators are continuously tested and verified multiple times. If the proportion of abnormal results exceeds a preset threshold, the water quality is confirmed to be abnormal and collaborative tracing is initiated; otherwise, no abnormality is determined. This method has the effect of reducing misjudgment and enhancing the reliability of abnormality confirmation. Multiple verifications reduce the risk of false positives caused by environmental fluctuations.

[0036] In one embodiment, the collaborative tracing module operates by including: The collaborative tracing module collects time-series data of sewage discharge flow corresponding to the sewage outlets that exceed the standard warning signal within a preset time interval. At the same time, it retrieves the water quality parameter change curves recorded by the river water quality monitoring unit within the same time period. The preset time interval can be set according to the transport time of pollutants in the river. Common values ​​are 1 to 24 hours. For example, if it takes 1 hour for sewage to be discharged from upstream to the downstream monitoring point, the preset time interval can be set to 2 hours before and 1 hour after the abnormal river water quality. Calculate the time difference between the peak flow time and the time of abrupt change in river water quality parameters for each sewage discharge outlet exceeding the standard. The time difference It can be calculated from the timestamps of both, combined with the distance between the sewage outlet and the water quality monitoring point. Through water flow speed Calculate the theoretical pollutant transport time ; like and If the deviation is within a preset fluctuation range, the corresponding sewage outlet exceeding the standard is marked as a sewage outlet with high spatiotemporal matching degree. The preset fluctuation range can be obtained by setting it according to the stability of river flow velocity, and a commonly used value is ±20%. ; For sewage outlets with high spatiotemporal matching, further analysis is conducted to determine whether their historical sewage quality data contains characteristic pollutants that are abnormal in the river. If matching characteristic pollutants are found, they are sorted by the product of concentration and flow rate of the characteristic pollutants to generate a list of suspected sources and corresponding pollution suspected source warning signals. If there are no sewage outlets exceeding the standards or no sewage outlets with high spatiotemporal matching within the preset time interval, the sewage outlet water quality detection unit will be activated first according to the industry risk level to obtain water quality parameter data of each sewage outlet. Then, the water quality parameter data of each sewage outlet and the river water quality parameter data will be compared and analyzed to determine whether there is a suspected source of pollution.

[0037] In the process of prioritizing the activation of wastewater outlet water quality testing units according to industry risk levels, the priority ranking rule is as follows: Chemicals > Pharmaceuticals > Printing and Dyeing > Food Processing > Domestic Sewage.

[0038] The process of determining whether a suspected source of pollution exists includes: Calculate the cosine similarity between the detected water quality parameters of the sewage outlet and the water quality parameters of the river channel. The formula is: in, The number of water quality indicators obtained from testing can be determined according to national / local monitoring standards. , The value of the i-th water quality indicator parameter for the wastewater discharged from the sewage outlet is obtained directly by the corresponding detection unit. The value of the i-th water quality index parameter corresponding to the river channel is obtained directly by the corresponding detection unit. If there exists a cosine similarity for a certain water quality indicator If the similarity index exceeds the preset similarity threshold and the water quality index in the river is in an abnormal state, the corresponding sewage outlet will be included in the list of suspected pollution sources, and a corresponding pollution suspected source warning signal will be generated. The preset similarity threshold can be set according to the correlation requirements of the index, and the commonly used value is 0.7~0.9. Conversely, if a suspected undetected sewage outlet is identified, an early warning signal for investigating hidden pollution sources is generated, prompting an expansion of the monitoring scope.

[0039] Through the above technical solution, this embodiment provides a method for collaboratively tracing suspected pollution sources. When river water quality is abnormal, the method collects flow time-series data of sewage outlets exceeding flow standards and river water quality parameter change curves through a collaborative tracing module. It calculates the time difference between the peak flow time and the time of water quality abrupt change, and combines the distance to the sewage outlet and water flow velocity to calculate the theoretical pollutant transport time. If the time difference deviates from the theoretical time within a preset fluctuation range, it is marked as a sewage outlet with high spatiotemporal matching. The matching degree of characteristic pollutants in its historical water quality is analyzed, and a list of suspected sources and a preliminary list are generated by sorting by concentration-flow product. If no matching sewage outlets exceeding the standard are found, the sewage outlet water quality detection unit is activated first according to the industry risk level (priority rule: chemical > pharmaceutical > printing and dyeing > food processing > domestic sewage). By calculating the cosine similarity between the sewage outlet and the river water quality parameters (the formula is based on the values ​​of each indicator parameter), if the similarity is greater than the preset threshold and the river indicators are abnormal, it is included in the list of suspected sources and an early warning is issued; otherwise, a hidden pollution source investigation early warning is generated. This method achieves the effect of efficiently locating pollution sources and optimizing the allocation of monitoring resources. By using spatiotemporal correlation and risk priority, the source tracing time is reduced and the accuracy of pollution source identification is improved.

[0040] This application also discloses a computer device, including a processor, wherein the processor runs a program for an environmental wastewater flow detection system as described in any one of the above embodiments.

[0041] This application also discloses a storage medium storing a program for an environmental wastewater flow detection system as described in any one of the above embodiments.

[0042] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An environmental wastewater flow detection system, characterized by, The system comprises: A sewage outlet flow detection unit arranged at each sewage outlet along the river bank for detecting sewage flow and generating flow time series data; A sewage outlet water quality detection unit arranged at each sewage outlet along the river bank for detecting sewage water quality parameters discharged by the sewage outlet; A river water quality monitoring unit arranged in the river downstream of the sewage outlet for detecting river water quality parameters; An intelligent analysis platform in communication connection with the flow detection unit, the sewage outlet water quality detection unit and the water quality monitoring unit, comprising: A flow analysis module for dynamically calculating the flow normal range of each sewage outlet based on historical data and judging whether the sewage discharge flow of each sewage outlet is normal; A river water quality analysis module for analyzing the detected river water quality parameters and judging whether the river water quality is abnormal; A collaborative traceability module for preferentially analyzing the spatiotemporal correlation between the flow over-limit sewage outlet and the water quality parameters when the river water quality is abnormal; An early warning module for executing the early warning signals generated in the working process of each module.

2. The environmental wastewater flow detection system of claim 1, wherein, The process of judging whether the sewage discharge flow of each sewage outlet is normal comprises: The flow analysis module collects historical flow time series data of each sewage outlet; In a preset sliding time window , calculate the average of the discharge flow of each discharge port and the standard deviation ; By formula The normal flow range of each discharge outlet is obtained by analysis and calculation ; Wherein, the is an adjustable sensitivity coefficient; The detected sewage flow data of each sewage outlet is compared with the normal flow interval of the sewage outlet performed​ If then it is determined that the sewage discharge of the sewage outlet is normal; If , it is determined that the sewage discharge of the sewage outlet exceeds the standard, and a warning signal of the sewage discharge of the sewage outlet exceeding the standard is generated. If If the sewage discharge of the sewage outlet is abnormal, it is determined that the sewage outlet flow detection unit needs to be checked whether it works normally.

3. The environmental wastewater flow detection system of claim 2, wherein, The process of checking whether the sewage outlet flow detection unit is working normally comprises: Step one: the intelligent analysis platform sends a self-checking instruction to the sewage outlet flow detection unit, and the instruction contains device operating state parameter acquisition requirements; Step two: the sewage outlet flow detection unit receives the self-checking instruction, performs self-checking on its core components, generates a self-checking report and feeds back to the intelligent analysis platform, and the self-checking report needs to contain the operating parameters of each component and the identification of whether it is working normally; Step three: the intelligent analysis platform analyzes the self-checking report, and if all core components are working normally, it further retrieves the historical operating data of the sewage outlet flow detection unit to check whether there are data anomaly records in a preset time period; Step four: if there are data anomaly records, the intelligent analysis platform controls the sewage outlet flow detection unit to perform calibration, which adopts a comparison with a standard flow device, records the deviation between the measured value and the standard value of the detection unit by injecting a fluid with a known flow, and if the deviation is within the allowable range, it is determined that the detection unit is working normally; Step five: if the calibration deviation exceeds the allowable range, or the self-checking report shows that the core components are abnormal, the intelligent analysis platform generates a device fault early warning signal to notify the operation and maintenance personnel to carry out on-site repair; Step six: after the operation and maintenance personnel complete the repair, they feed back the repair results to the intelligent analysis platform, the intelligent analysis platform controls the sewage outlet flow detection unit to restart and perform a flow detection, compares the detection results with the normal range, and if it returns to normal, the checking is ended; if it is still abnormal, the above steps are repeated for secondary checking.

4. The environmental wastewater flow detection system of claim 3, wherein, The process of judging whether the river water quality is abnormal comprises: The river water quality analysis module collects the river water quality parameter values of each item detected by the river water quality monitoring unit; The detected water quality parameter values of each item are compared with the allowable upper and lower limit values corresponding to the water quality index, and if any water quality parameter value is not within the allowable upper and lower limit value range corresponding to the water quality index, it is preliminarily determined that the water quality detection of the water quality index is abnormal. If the proportion of the detection result of the water quality index anomaly in the multiple continuous detection verification results exceeds the preset proportion threshold, it is determined that the river water quality is abnormal, and the cooperative tracing module is started, otherwise, it is not determined that the river water quality is abnormal.

5. The environmental wastewater flow detection system of claim 4, wherein, The working process of the cooperative tracing module includes: The cooperative tracing module collects the sewage discharge flow time series data corresponding to the excessive warning signal generated by the flow analysis module within a preset time interval, and simultaneously calls the water quality parameter change curve recorded by the river water quality monitoring unit in the time period; Calculate the time difference between the peak flow time and the time of abrupt change in river water quality parameters for each sewage discharge outlet exceeding the standard. Combining the distance between the sewage outlet and the water quality monitoring point Through water flow speed Calculate the theoretical pollutant transport time ; If With the deviation is within a preset fluctuation range, mark the corresponding over-standard sewage outlet as a high spatio-temporal matching degree sewage outlet. For high spatio-temporal matching degree sewage outlets, further analyze whether the historical sewage water quality data contains characteristic pollutants of river anomalies, if there are matching characteristic pollutants, sort by characteristic pollutant concentration-flow product, generate a list of suspected sources, and generate a corresponding pollution suspected source warning signal; If there is no excessive sewage outlet or no high spatio-temporal matching degree sewage outlet within the preset time interval, the sewage outlet water quality detection unit is started according to the industry risk level priority, and the water quality parameter data of each sewage outlet is obtained, and then the water quality parameter data of each sewage outlet and the river water quality parameter data are compared and analyzed to determine whether there is a pollution suspected source.

6. The environmental wastewater flow detection system of claim 5, wherein, In the process of starting the sewage outlet water quality detection unit according to the industry risk level priority, the priority sorting rule is: Chemical industry > pharmaceutical industry > printing and dyeing > food processing > domestic sewage.

7. The environmental wastewater flow detection system of claim 6, wherein, The process of determining whether there is a pollution suspected source includes: The cosine similarity between each water quality index parameter value of the detected sewage outlet and each water quality index parameter value of the river is calculated in sequence , and the formula is: wherein, is the number of water quality index items obtained for detecting, , is the i-th water quality index parameter value of sewage discharged from the sewage outlet, is the i-th water quality index parameter value corresponding to the river channel; If there exists a cosine similarity for a certain water quality indicator If the similarity index exceeds the preset threshold and the water quality index in the river is in an abnormal state, the corresponding sewage outlet will be included in the list of suspected pollution sources, and a corresponding pollution source warning signal will be generated. Otherwise, it is determined that there is a suspected sewage outlet that has not been monitored, a hidden pollution source investigation warning signal is generated, and the monitoring range is expanded.

8. The environmental wastewater flow detection system of claim 1, wherein, The sewage outlet flow detection unit includes an anti-blocking ultrasonic flowmeter, the front end of which is provided with a conical flow guide cover, and a rotating self-cleaning brush is built-in.

9. A computer device, comprising: A processor, wherein the processor runs a program of an environmental sewage flow detection system according to any one of claims 1-8.

10. A storage medium, characterized by A storage device storing a program of an environmental sewage flow detection system according to any one of claims 1-8.

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