Petrochemical wastewater discharge detection method based on multi-source data analysis

Abstract

The invention relates to the technical field of wastewater discharge detection, and discloses a petrochemical wastewater discharge detection method based on multi-source data analysis, and the method comprises the steps: firstly, deploying an environment sensor at each drainage outlet, and monitoring various physical and chemical parameters of water quality to obtain multi-source data; the collected data is preprocessed, including noise filtering, abnormal value recognition and correction, missing value filling and normalization, so that the data quality is improved; further, analyzing the processed data, respectively extracting a first characteristic index reflecting the current instantaneous water quality state and a second characteristic index reflecting the cumulative emission trend in a time period, and quantifying the first characteristic index and the second characteristic index by calculating the cosine similarity of a real-time state vector and a standard state vector; and finally, intelligent diagnosis is carried out based on the key characteristic indexes, and a three-level early warning mechanism is adopted to carry out graded evaluation and early warning on the sewage discharge risk, so that accurate and automatic monitoring on the discharge is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wastewater discharge detection, and particularly relates to a petrochemical wastewater discharge detection method based on multi-source data analysis. BACKGROUND

[0002] The petrochemical industry is an important pillar industry of the national economy, and a large amount of wastewater is generated in the production process. The existing chemical wastewater discharge detection has the following problems: 1. Relies on manual sampling and laboratory analysis: leads to long detection cycle, high cost, and cannot realize real-time monitoring, and it is difficult to discover sudden pollution events in time; 2. Single parameter threshold alarm: the traditional online monitoring system usually only sets a fixed threshold for a single water quality parameter (such as COD, pH), and alarms when the data exceeds the threshold. This method cannot capture the synergistic change relationship between multiple parameters, and is not sensitive to complex pollution or abnormal water quality mode when the concentration does not exceed the standard, and is prone to false negatives; 3. Lack of trend analysis and intelligent diagnosis: the existing technology often focuses on the monitoring of instantaneous data, lacks deep mining and intelligent analysis of the trend of data change over time, and cannot early warn potential and cumulative emission risks.

[0003] Therefore, the present application provides a petrochemical wastewater discharge detection method based on multi-source data analysis, aiming at solving the above technical problems. SUMMARY

[0004] The present application aims to provide a petrochemical wastewater discharge detection method based on multi-source data analysis, which solves the above technical problems.

[0005] A petrochemical wastewater discharge detection method based on multi-source data analysis, the method comprising the following steps: Step S1, deploying environmental sensors at each drainage outlet to monitor physical and chemical parameters of water quality in real time and continuously; Step S2, processing the collected data; Step S3, analyzing the processed data to extract key feature indicators of water quality; Step S4, based on the key feature indicators of water quality, intelligently diagnosing the sewage discharge, and issuing a risk warning according to the diagnosis result.

[0006] As a further description of the technical scheme of the present application, the working process of step S2 comprises: For noisy data, filtering algorithms are used to remove high-frequency noise and interference signals. For outliers, reasonable data thresholds and statistical analysis methods are used to identify and correct them. For missing values, linear interpolation is used to fill them in based on the time series characteristics and correlation of the data. At the same time, in order to make different types of data comparable and facilitate subsequent analysis and modeling, the data is normalized.

[0007] As a further description of the technical solution of the present invention, the working process of step S3 includes: Step S31: Obtain various water quality parameter data for each drainage outlet at the current time; Step S32: Extract the first state characteristic indicators of wastewater at each drainage outlet at the current time based on the water quality parameters of each drainage outlet at the current time; Step S33: Obtain the data on the changes of various water quality parameters at each drainage outlet over time during the current time period; Step S34: Extract the second state feature index based on the time-varying data of various water quality parameters of each drainage outlet in the current time period.

[0008] As a further description of the technical solution of the present invention, the working process of step S32 includes: Obtain real-time data of various water quality parameters of the wastewater from the i-th drainage outlet at the current time, and construct the first real-time state vector of the wastewater from the i-th drainage outlet based on the real-time data of various water quality parameters of the wastewater from the i-th drainage outlet. A first standard state vector is constructed by obtaining the standard state data of various water quality parameters of the wastewater from the i-th drainage outlet set by the system. Calculate vector sum vector cosine similarity The cosine similarity This refers to the first state characteristic index of the wastewater at the i-th drainage outlet.

[0009] As a further description of the technical solution of the present invention, the working process of step S34 includes: Obtain the time-varying data of each water quality parameter of the i-th drainage outlet during the current time period, and obtain the cumulative discharge of each water quality parameter of the i-th drainage outlet during the current time period. Construct the second real-time state vector of the wastewater of the i-th drainage outlet based on the cumulative discharge of each water quality parameter of the i-th drainage outlet during the current time period. A second standard state vector is constructed by obtaining the cumulative discharge standard state data of various water quality parameters of the wastewater from the i-th drainage outlet within a set time period. Calculate vector sum vector cosine similarity The cosine similarity This refers to the second state characteristic index of the wastewater at the i-th drainage outlet.

[0010] As a further description of the technical solution of the present invention, the working process of step S4 includes: The system compares each water quality parameter of the wastewater from the i-th drainage outlet with the standard range of each water quality parameter set by the system. If any water quality parameter does not meet the corresponding standard range, it means that the wastewater from the i-th drainage outlet does not meet the discharge standard, and a first-level warning is issued. If no water quality parameter fails to meet the corresponding standard range, the first state characteristic index of the wastewater at the i-th discharge outlet will be... Compared with the standard value set by the system Comparison, if the first state characteristic index of the wastewater at the i-th drainage outlet... Lower than the corresponding standard value set by the system If the wastewater from the i-th drainage outlet does not meet the discharge standards, a level two warning will be issued. If the first state characteristic index of the wastewater at the i-th drainage outlet is... Greater than or equal to the corresponding standard value set by the system The second state characteristic index of the wastewater at the i-th drainage outlet Compared with the standard value set by the system Comparison, if the second state characteristic index of the wastewater at the i-th drainage outlet... Lower than the corresponding standard value set by the system If the result is positive, it indicates that the wastewater from the i-th drainage outlet does not meet the discharge standards, and a level three warning will be issued.

[0011] The beneficial effects of this invention are: This invention provides a method for detecting petrochemical wastewater discharge based on multi-source data analysis. The method first deploys environmental sensors at various drainage outlets to monitor multiple physicochemical parameters of the water quality and obtain multi-source data. Then, the collected data is preprocessed, including noise filtering, outlier identification and correction, missing value filling, and normalization to improve data quality. Next, the processed data is analyzed to extract a first characteristic indicator reflecting the current instantaneous water quality state and a second characteristic indicator reflecting the cumulative discharge trend over a time period. Both are quantified by calculating the cosine similarity between the real-time state vector and the standard state vector. Finally, based on these key characteristic indicators, intelligent diagnosis is performed, and a three-level early warning mechanism is used to classify and assess the risk of wastewater discharge, thereby achieving precise and automated monitoring of its discharge. Attached Figure Description

[0012] The invention will now be further described with reference to the accompanying drawings.

[0013] Figure 1This is a partial flowchart of the petrochemical wastewater discharge detection method based on multi-source data analysis provided by the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Please see Figure 1 As shown, a method for detecting petrochemical wastewater discharge based on multi-source data analysis is provided, the method comprising the following steps: Step S1: Deploy environmental sensors at each drainage outlet to monitor the physicochemical parameters of water quality in real time and continuously. Step S2: Process the collected data; Step S3: Analyze the processed data and extract key water quality characteristic indicators; Step S4: Based on key water quality indicators, perform intelligent diagnosis of wastewater discharge and issue risk warnings based on the diagnosis results.

[0016] Through the above technical solution, this invention monitors the physicochemical parameters of water quality in real time and continuously using environmental sensors deployed at drainage outlets, forming multi-source data input. First, the collected raw data is preprocessed, including using filtering algorithms to remove high-frequency noise and interference signals, identifying and correcting outliers through threshold setting and statistical analysis, filling missing values ​​using linear interpolation, and performing normalization to improve data comparability and modeling effectiveness. Subsequently, in the data analysis stage, key water quality characteristic indicators are extracted for the current moment and the current time period: for the current moment data, a real-time state vector and a standard state vector for each drainage outlet are constructed, and their cosine similarity is calculated as the first state characteristic indicator, reflecting the degree of deviation of instantaneous water quality from the standard; for the cumulative data within the time period, a second real-time state vector is constructed based on the cumulative discharge of water quality parameters over time, and its cosine similarity with the standard cumulative state vector is calculated as the second state characteristic indicator, reflecting whether the long-term discharge trend meets the requirements. Finally, in the intelligent diagnosis and early warning stage, a tiered judgment mechanism is adopted: if any water quality parameter exceeds the preset standard range, a Level 1 warning is immediately triggered; if all parameters are within the range, but the first state characteristic indicator is lower than the set threshold, a Level 2 warning is issued; if the first indicator meets the standard, but the second state characteristic indicator is lower than the corresponding threshold, a Level 3 warning is triggered. This method, through multi-dimensional and multi-timescale data fusion and intelligent analysis, achieves real-time, accurate, and tiered risk identification and early warning for petrochemical wastewater discharge, effectively improving the automation and intelligence level of discharge supervision.

[0017] As a further description of the technical solution of the present invention, the working process of step S2 includes: For noisy data, filtering algorithms are used to remove high-frequency noise and interference signals. For outliers, reasonable data thresholds and statistical analysis methods are used to identify and correct them. For missing values, linear interpolation is used to fill them in based on the time series characteristics and correlation of the data. At the same time, in order to make different types of data comparable and facilitate subsequent analysis and modeling, the data is normalized.

[0018] As a further description of the technical solution of the present invention, the working process of step S3 includes: Step S31: Obtain various water quality parameter data for each drainage outlet at the current time; Step S32: Extract the first state characteristic indicators of wastewater at each drainage outlet at the current time based on the water quality parameters of each drainage outlet at the current time; Step S33: Obtain the data on the changes of various water quality parameters at each drainage outlet over time during the current time period; Step S34: Extract the second state feature index based on the time-varying data of various water quality parameters of each drainage outlet in the current time period.

[0019] As a further description of the technical solution of the present invention, the working process of step S32 includes: Obtain real-time data of various water quality parameters of the wastewater from the i-th drainage outlet at the current time, and construct the first real-time state vector of the wastewater from the i-th drainage outlet based on the real-time data of various water quality parameters of the wastewater from the i-th drainage outlet. A first standard state vector is constructed by obtaining the standard state data of various water quality parameters of the wastewater from the i-th drainage outlet set by the system. Calculate vector sum vector cosine similarity The cosine similarity This refers to the first state characteristic index of the wastewater at the i-th drainage outlet.

[0020] As a further description of the technical solution of the present invention, the working process of step S34 includes: Obtain the time-varying data of each water quality parameter of the i-th drainage outlet during the current time period, and obtain the cumulative discharge of each water quality parameter of the i-th drainage outlet during the current time period. Construct the second real-time state vector of the wastewater of the i-th drainage outlet based on the cumulative discharge of each water quality parameter of the i-th drainage outlet during the current time period. A second standard state vector is constructed by obtaining the cumulative discharge standard state data of various water quality parameters of the wastewater from the i-th drainage outlet within a set time period. Calculate vector sum vector cosine similarity The cosine similarity This refers to the second state characteristic index of the wastewater at the i-th drainage outlet.

[0021] Through the above technical solution, this embodiment constructs a two-dimensional water quality assessment model based on cosine similarity, which quantifies the discharge compliance of drainage outlets from two levels: "instantaneous state" and "cumulative trend". Its core working principle lies in transforming abstract, multi-parameter water quality data into a computable vector, and determining the overall health level by calculating its directional similarity (i.e., cosine similarity) with a standard vector. Specifically, the system first assesses the current instantaneous state: it acquires real-time readings of multiple water quality parameters (such as pH, chemical oxygen demand, heavy metal concentration, etc.) for the i-th drainage outlet at the current moment, and combines these readings into a first real-time state vector. Simultaneously, the system presets a first standard state vector for the parameters corresponding to the drainage outlet under ideal compliance conditions. Subsequently, the vector is calculated... sum vector cosine similarity Cosine similarity does not focus on the absolute magnitude of the vectors (i.e., the absolute value of emission concentration or total amount), but rather on whether their directions are consistent. If the directions are consistent (cosine value close to 1), it indicates that the current proportions of water quality parameters are highly consistent with the standard state, belonging to the "normal mode." If the directions deviate (cosine value decreases), it indicates that even if individual parameters do not exceed the standard, the inherent balance between parameters has been broken, possibly indicating complex compound pollution or abnormal emissions. This serves as the first state characteristic indicator. Then, it further performs in-depth monitoring from a time-cumulative dimension: it acquires the sequence data of various water quality parameters changing over time for the i-th discharge outlet within the "current time period" (e.g., the past 24 hours), and calculates the cumulative discharge of each parameter within that time period, thereby constructing a second real-time state vector. Similarly, the system presets a second standard state vector (representing a standard cumulative discharge pattern) allowed within a set time period. (Vector calculation) sum vector cosine similarity This indicator The aim is to assess whether the emission "behavioral patterns" of wastewater outlets are abnormal over a period of time. For example, even if no alarm is triggered at any given moment, a rapid cumulative emission rate of a certain pollutant, or a deviation in its cumulative proportion relative to other parameters from the standard pattern, will lead to a decrease in the cosine similarity F. This effectively identifies "illegal discharge" behaviors that attempt to evade instantaneous detection through intermittent, low-dose emissions. In summary, this embodiment, by introducing a cosine similarity algorithm, upgrades the detection from static, single-parameter threshold judgment to dynamic, multi-parameter collaborative pattern recognition, greatly improving the sensitivity, comprehensiveness, and anti-evasion capabilities of petrochemical wastewater discharge detection.

[0022] As a further description of the technical solution of the present invention, the working process of step S4 includes: The system compares each water quality parameter of the wastewater from the i-th drainage outlet with the standard range of each water quality parameter set by the system. If any water quality parameter does not meet the corresponding standard range, it means that the wastewater from the i-th drainage outlet does not meet the discharge standard, and a first-level warning is issued. If no water quality parameter fails to meet the corresponding standard range, the first state characteristic index of the wastewater at the i-th discharge outlet will be... Compared with the standard value set by the system Comparison, if the first state characteristic index of the wastewater at the i-th drainage outlet... Lower than the corresponding standard value set by the system If the wastewater from the i-th drainage outlet does not meet the discharge standards, a level two warning will be issued. If the first state characteristic index of the wastewater at the i-th drainage outlet is... Greater than or equal to the corresponding standard value set by the system The second state characteristic index of the wastewater at the i-th drainage outlet Compared with the standard value set by the system Comparison, if the second state characteristic index of the wastewater at the i-th drainage outlet... Lower than the corresponding standard value set by the system If the result is positive, it indicates that the wastewater from the i-th drainage outlet does not meet the discharge standards, and a level three warning will be issued.

[0023] This embodiment establishes a three-level progressive early warning mechanism based on the above technical solution. Through progressively in-depth intelligent diagnosis, it accurately determines the compliance of wastewater discharge and differentiates risk levels. The mechanism first performs a level-one early warning judgment, directly comparing the real-time measured values ​​of all water quality parameters at the i-th discharge outlet with the system's preset standard range. If any parameter exceeds the standard, the highest level, level-one early warning, is immediately triggered, indicating a clear and immediate violation of discharge regulations. If all parameters are within the standard range, the system proceeds to a level-two early warning judgment, examining the first state characteristic indicators. ,like Below the threshold This indicates that although the water quality did not exceed the standard, its overall condition had deviated from the normal pattern, posing a potential risk, thus triggering a level-two warning. If i is also qualified, the system will eventually initiate a level three warning judgment, examining the second state characteristic indicators. ,like Below the threshold This indicates the possible existence of hidden, persistent abnormal emissions, thus triggering a Level 3 warning. This working principle enables a comprehensive detection system that progresses from "parameter exceeding limits" to "abnormal status" and then to "abnormal behavior," greatly improving the accuracy and foresight of supervision.

[0024] It should be noted that the formulas in this application are all dimensionless and numerical calculations. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The thresholds and weight coefficients involved in this application are all empirical values ​​and are selected by those skilled in the art according to the actual situation.

[0025] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for detecting petrochemical wastewater discharge based on multi-source data analysis, characterized in that, The method includes the following steps: Step S1: Deploy environmental sensors at each drainage outlet to monitor the physicochemical parameters of water quality in real time and continuously. Step S2: Process the collected data; Step S3: Analyze the processed data and extract key water quality characteristic indicators; Step S4: Based on key water quality indicators, perform intelligent diagnosis of wastewater discharge and issue risk warnings based on the diagnosis results.

2. The method for detecting petrochemical wastewater discharge based on multi-source data analysis according to claim 1, characterized in that, The working process of step S2 includes: For noisy data, filtering algorithms are used to remove high-frequency noise and interference signals. For outliers, reasonable data thresholds and statistical analysis methods are used to identify and correct them. For missing values, linear interpolation is used to fill them in based on the time series characteristics and correlation of the data. At the same time, in order to make different types of data comparable and facilitate subsequent analysis and modeling, the data is normalized.

3. The method for detecting petrochemical wastewater discharge based on multi-source data analysis according to claim 1, characterized in that, The working process of step S3 includes: Step S31: Obtain various water quality parameter data for each drainage outlet at the current time; Step S32: Extract the first state characteristic indicators of wastewater at each drainage outlet at the current time based on the water quality parameters of each drainage outlet at the current time; Step S33: Obtain the data on the changes of various water quality parameters at each drainage outlet over time during the current time period; Step S34: Extract the second state feature index based on the time-varying data of various water quality parameters of each drainage outlet in the current time period.

4. The method for detecting petrochemical wastewater discharge based on multi-source data analysis according to claim 3, characterized in that, The working process of step S32 includes: Obtain real-time data of various water quality parameters of the wastewater from the i-th drainage outlet at the current time, and construct the first real-time state vector of the wastewater from the i-th drainage outlet based on the real-time data of various water quality parameters of the wastewater from the i-th drainage outlet. A first standard state vector is constructed by obtaining the standard state data of various water quality parameters of the wastewater from the i-th drainage outlet set by the system. Calculate vector sum vector cosine similarity The cosine similarity This refers to the first state characteristic index of the wastewater at the i-th drainage outlet.

5. The method for detecting petrochemical wastewater discharge based on multi-source data analysis according to claim 3, characterized in that, The working process of step S34 includes: Obtain the time-varying data of each water quality parameter of the i-th drainage outlet during the current time period, and obtain the cumulative discharge of each water quality parameter of the i-th drainage outlet during the current time period. Construct the second real-time state vector of the wastewater of the i-th drainage outlet based on the cumulative discharge of each water quality parameter of the i-th drainage outlet during the current time period. A second standard state vector is constructed by obtaining the cumulative discharge standard state data of various water quality parameters of the wastewater from the i-th drainage outlet within a set time period. Calculate vector sum vector cosine similarity The cosine similarity This refers to the second state characteristic index of the wastewater at the i-th drainage outlet.

6. The method for detecting petrochemical wastewater discharge based on multi-source data analysis according to claim 1, characterized in that, The working process of step S4 includes: The system compares each water quality parameter of the wastewater from the i-th drainage outlet with the standard range of each water quality parameter set by the system. If any water quality parameter does not meet the corresponding standard range, it means that the wastewater from the i-th drainage outlet does not meet the discharge standard, and a first-level warning is issued. If no water quality parameter fails to meet the corresponding standard range, the first state characteristic index of the wastewater at the i-th discharge outlet will be... Compared with the standard value set by the system Comparison, if the first state characteristic index of the wastewater at the i-th drainage outlet... Lower than the corresponding standard value set by the system If the wastewater from the i-th drainage outlet does not meet the discharge standards, a level two warning will be issued. If the first state characteristic index of the wastewater at the i-th drainage outlet is... Greater than or equal to the corresponding standard value set by the system The second state characteristic index of the wastewater at the i-th drainage outlet Compared with the standard value set by the system Comparison, if the second state characteristic index of the wastewater at the i-th drainage outlet... Lower than the corresponding standard value set by the system If the result is positive, it indicates that the wastewater from the i-th drainage outlet does not meet the discharge standards, and a level three warning will be issued.

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