CN115204669A - Sewage treatment plant behavior abnormity determination method and system based on electricity consumption data - Google Patents
Sewage treatment plant behavior abnormity determination method and system based on electricity consumption data Download PDFInfo
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Abstract
The invention discloses a sewage treatment plant behavior abnormity judgment method and system based on electricity consumption data, and relates to the field of sewage treatment monitoring, wherein the judgment method comprises the following steps: acquiring historical data in a sewage treatment plant to be monitored, and performing data cleaning on the historical data to obtain historical cleaning data; calculating the weight coefficient of each index in the historical cleaning data, and selecting three indexes with the maximum weight coefficient as behavior indexes; constructing a behavior abnormity judgment model according to the behavior indexes; according to the judgment result of the behavior abnormity judgment model, the credibility index of the behavior abnormity judgment is established to obtain the credibility of the behavior abnormity judgment, so that the accuracy of the judgment result is improved, the behavior abnormity condition of the sewage treatment plant is mastered according to the electricity utilization condition of the sewage treatment plant, the situation of one operation is avoided, and the refinement level of monitoring is improved.
Description
Technical Field
The invention relates to sewage treatment monitoring, in particular to a sewage treatment plant behavior abnormity judgment method and system based on electricity consumption data.
Background
With the further acceleration of urbanization construction, the environmental pollution problem of the villages and towns is increasingly prominent, and the effective operation of sewage treatment plants in the villages and towns is an important guarantee for the sustainable development of the villages and towns.
The existing sewage treatment plant abnormal operation judgment method has great limitation, only considers the scale, treatment process or total annual sewage treatment amount of the sewage treatment plant, the sewage treatment plant with abnormal electricity utilization cannot be identified, the abnormal operation condition cannot be judged according to the electricity utilization condition of the sewage treatment plant, and the result accuracy is not high; due to the fact that different areas and different scales of sewage treatment plant power utilization habits are different, and uncertain factors exist in part of sewage treatment plants, the power utilization characteristics of the sewage treatment plants which operate abnormally are different, and the critical value of abnormal operation cannot be given according to the power utilization level and the power utilization condition of the sewage treatment plants under different actual scales, so that the abnormal behavior of the sewage treatment plants can be accurately judged.
Disclosure of Invention
The invention aims to solve the technical problems of improving the accuracy of judging the abnormal operation of the sewage treatment plant and improving the refinement level of monitoring and supervision, and aims to provide a method and a system for judging the abnormal behavior of the sewage treatment plant based on electricity utilization data, so that the problems that the traditional method for judging the abnormal operation of the sewage treatment plant has limitation and the abnormal operation of the sewage treatment plant cannot be accurately judged are solved.
The invention is realized by the following technical scheme:
the first aspect provides a method for judging abnormal behavior of a sewage treatment plant based on electricity utilization data, which comprises the following steps:
acquiring historical data in a sewage treatment plant to be monitored, and performing data cleaning on the historical data to obtain historical cleaning data;
calculating the weight coefficient of each index in the historical cleaning data, and selecting three indexes with the maximum weight coefficient as behavior indexes;
constructing a behavior abnormity judgment model according to the behavior indexes;
and establishing a reliability index of the behavior abnormity judgment according to the judgment result of the behavior abnormity judgment model to obtain the reliability of the behavior abnormity judgment.
And carrying out importance analysis on each index according to the actual condition of the sewage treatment plant, selecting the index which has a larger influence on the abnormal behavior of the sewage treatment plant as a behavior index to establish a behavior abnormity judgment model, and calculating the reliability of a judgment result according to the severity of the abnormal behavior of the sewage treatment plant to realize accurate evaluation on the abnormal behavior of the sewage treatment plant.
Further, the indexes comprise actual treatment scale, total annual sewage treatment amount, sewage treatment process, annual sewage treatment rate, design treatment scale, accumulated finished sewage pipe network length, discharge standard and construction operation state.
Further, the historical data includes daily electricity consumption data and daily indicating value data, and the step of performing data cleaning on the daily electricity consumption data in the historical data includes:
judging whether the daily electric quantity data has missing data or not;
if the missing data exists, marking the missing data, acquiring the current day indicating value data, judging whether the current day indicating value data is missing or not,
if the daily indicating value data is missing, calculating the average value of the daily electricity consumption data of days adjacent to the daily electricity consumption data, recording the average value at a mark, supplementing the missing data, and returning to the step of judging whether the daily electricity consumption data has missing data for continuous execution;
if the daily indicating value data is not missing, calculating the difference value of the daily indicating value data, recording the difference value at a mark, supplementing the missing data, and returning to the step of judging whether the daily electric quantity data has the missing data for continuous execution;
and if the missing data does not exist, obtaining historical cleaning data.
According to the daily electric quantity data in the historical data, the power utilization state of the sewage treatment plant when the behaviors are abnormal is mastered, so that the situation is avoided, the fine monitoring level is improved, the daily electric quantity data is subjected to data cleaning, the daily electric quantity data is corrected and supplemented, the precision of the index weight coefficient is improved, and the accuracy of the judgment result is improved.
Further, before calculating the weight coefficient of each index in the historical cleaning data, the data of the actual treatment scale and the data of the annual total sewage treatment amount need to be corrected or supplemented, and the method comprises the following steps:
judging whether the total amount of the annual sewage treatment and the actual treatment scale meet the conditions, wherein the conditions are as follows:
S i ×365＜W i x 0.8 or S i ×365＞W i ×1.2
Wherein S is i Data showing the actual treatment scale of the ith sewage treatment plant, W i Represents annual total sewage treatment data of the ith sewage treatment plant;
if S i And W i Having a solution, satisfying the above conditionsThen according to
Modifying the actual processing scale data;
if S i Or W i If no solution is available, the above condition is not satisfied, then
And supplementing the corresponding actual treatment scale data or annual total sewage treatment data.
Further, according to the weight coefficient of each index, the relative membership degree is calculated, the indexes are subjected to weight sorting, and three indexes with larger weight coefficients are selected as behavior indexes.
Further, according to the weight coefficient of each index, calculating the relative membership degree, wherein the specific formula is as follows:
wherein,
wherein u is j Is a relative degree of membership, B p Is a feature matrix, w i Is the weight coefficient of the i-th index, A u To determine the matrix, a ij Is the firstThe value range of the relative value of the i indexes to the j index is [1,9 ]]And reciprocal thereof, b ij Is the score of the j index to the i index, r ij Is the relative degree of membership of the jth index to the ith index.
Further, the behavior abnormality determination model includes one or more abnormality determinations, and includes the following steps:
judging whether all the daily electric quantity data in the sewage treatment plant are 0;
if all the electricity consumption values are 0, judging that the daily electricity consumption is abnormal by the sewage treatment plant;
if not all the daily electric quantity is 0, judging that the daily electric quantity is normal by the sewage treatment plant;
calculating the ton water power consumption of the sewage treatment plant according to the actual treatment scale data and daily electricity consumption data every day;
dividing the sewage treatment plants into a plurality of categories according to actual treatment scale, and calculating the average value of the ton water power consumption of all the sewage treatment plants under each category;
judging whether the ton water power consumption of the sewage treatment plant under the category is continuously less than the average value of 0.8 times of the ton water power consumption;
if so, judging that the power consumption per ton of water is abnormal by the sewage treatment plant;
if not, the power consumption per ton of water of the sewage treatment plant is normal;
calculating the variation coefficient of the daily electricity quantity data of the sewage treatment plant, and judging whether the variation coefficient is more than 0.36;
if the current power consumption is larger than the preset power consumption, judging that the daily power consumption fluctuation is abnormal by the sewage treatment plant;
if the power consumption is not more than the preset value, the daily power fluctuation of the sewage treatment plant is normal.
Further, the determination result includes one or more of abnormality of daily electricity consumption, abnormality of ton water electricity consumption, and abnormality of daily electricity fluctuation;
the judgment result comprises three abnormal behavior levels, and the abnormal daily electricity consumption is a first-level abnormal behavior;
if the power consumption per ton of water is abnormal and the daily power fluctuation is abnormal, the second-level behavior is abnormal;
and if any one of the abnormality of the power consumption per ton of water and the abnormality of the daily power fluctuation is abnormal, the three-level behavior is abnormal.
Further, judging the behavior abnormity level of the sewage treatment plant, and calculating the behavior abnormity judgment reliability according to the behavior abnormity level of the sewage treatment plant;
if the sewage treatment plant is abnormal in primary behavior, the reliability of judging the abnormal behavior is 100 percent;
if the sewage treatment plant is abnormal in secondary behaviors, the calculation formula of judging the reliability of the abnormal behaviors is as follows:
if the sewage treatment plant is abnormal in third-level behaviors, the calculation formula of judging the reliability of the abnormal behaviors is as follows:
wherein, P Ti Represents the abnormal judgment reliability, SR, of the ith sewage treatment plant in the statistical time period T Ti Shows that the actual treatment scale of the ith sewage treatment plant and the daily electric quantity reach the equilibrium value H in the equilibrium state within the statistical time period T Ti Represents the ton water power consumption H of the ith sewage treatment plant in the statistical time period T mean Means of the ton water power consumption, cv, under this category Ti And the variation coefficient of the daily electricity consumption data of the ith sewage treatment plant in the statistical time period T is shown.
The reliability of the behavior abnormity judgment adopts a corresponding calculation mode according to different grades of the behavior abnormity, so that the accuracy of the judgment result is improved.
A second aspect provides a sewage treatment plant behavior abnormality determination system based on electricity consumption data, the determination system being used for implementing the above-mentioned sewage treatment plant behavior abnormality determination method based on electricity consumption data, the determination system comprising:
the acquisition unit is used for acquiring historical data in a sewage treatment plant to be monitored;
a processing unit for processing the received data,
the historical data is subjected to data cleaning to obtain historical cleaning data;
calculating the weight coefficient of each index in the historical cleaning data, and selecting three indexes with the maximum weight coefficient as behavior indexes;
constructing a behavior abnormity judgment model according to the behavior indexes;
establishing a reliability index of the behavior abnormity judgment according to the judgment result of the behavior abnormity judgment model to obtain the reliability of the behavior abnormity judgment;
and the output unit is used for outputting the judgment reliability of the abnormal behavior of the sewage treatment plant to be monitored.
The actual conditions of the sewage treatment plant are collected through the collecting unit, importance analysis is carried out on all indexes in the processing unit according to collected data, the index which has a large influence on the abnormal behavior of the sewage treatment plant is selected as the behavior index to establish a behavior abnormity judgment model, the behavior abnormity judgment reliability of the judgment result is calculated according to the behavior abnormity grade of the judgment result, and the accuracy of the behavior abnormity judgment of the sewage treatment plant is improved.
Compared with the prior art, the invention has the following advantages and beneficial effects:
according to the actual condition of the sewage treatment plant, importance analysis is carried out on each index, the index which has a large influence on the abnormal behavior of the sewage treatment plant is selected as a behavior index to establish a behavior abnormity judgment model, and according to the severity of the abnormal behavior of the sewage treatment plant, the reliability of a judgment result is calculated, so that the accurate evaluation of the abnormal behavior condition of the sewage treatment plant is realized;
compared with the traditional monitoring mode, the invention grasps the abnormal behavior condition of the sewage treatment plant according to the electricity utilization condition of the sewage treatment plant, avoids cutting once, and improves the refinement level of monitoring and supervision.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are required in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that those skilled in the art may also derive other related drawings based on these drawings without inventive effort. In the drawings:
FIG. 1 is a flow chart provided in example 1;
fig. 2 is a system block diagram provided in embodiment 2.
Reference numbers and corresponding part names in the drawings:
1-acquisition unit, 2-processing unit and 3-output unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
The embodiment 1 provides a method for determining abnormal behavior of a sewage treatment plant based on electricity consumption data, which comprises the following steps as shown in fig. 1:
s1, acquiring historical data in a sewage treatment plant to be monitored, and performing data cleaning on the historical data to obtain historical cleaning data;
s2, calculating the weight coefficient of each index in the historical cleaning data, and selecting three indexes with larger weight coefficients as behavior indexes;
s3, constructing a behavior abnormity judgment model according to the behavior indexes;
and S4, establishing a reliability index of the behavior abnormity judgment according to the judgment result of the behavior abnormity judgment model to obtain the reliability of the behavior abnormity judgment.
And carrying out importance analysis on each index according to the actual condition of the sewage treatment plant, selecting the index which has a larger influence on the abnormal behavior of the sewage treatment plant as a behavior index to establish a behavior abnormity judgment model, and calculating the reliability of a judgment result according to the severity of the abnormal behavior of the sewage treatment plant to realize accurate evaluation on the abnormal behavior of the sewage treatment plant.
In a specific embodiment, the indexes include actual treatment scale, total annual sewage treatment amount, sewage treatment process, annual sewage treatment rate, design treatment scale, cumulative finished sewage pipe network length, discharge standard and construction operation state.
In a specific embodiment, the actual processing scale includes 11 categories: more than 1000 tons/day, 901-1000 tons/day, 801-900 tons/day, 701-800 tons/day, 601-700 tons/day, 501-600 tons/day, 401-500 tons/day, 301-400 tons/day, 201-300 tons/day, 101-200 tons/day and less than 100 tons/day, the data of the actual processing scale of the sewage treatment plant is positively correlated with the data of the daily electric quantity, and the larger the actual processing scale is, the larger the daily electric quantity is;
the more complex the sewage treatment process is, the longer the treatment time is, and the higher the daily electricity consumption is;
and (3) building an operation state, wherein the operation condition of the sewage treatment plant is divided into the following steps according to regional characteristics: the method comprises the steps of shutdown, debugging and commissioning, completion of a main body, start-up, formal operation and normal operation. Regarding finished, main body finished, operation started, formal operation and normal operation as normal behavior; and (5) regarding the shutdown, the allocation and the test operation as abnormal behaviors.
In a specific embodiment, the historical data includes daily electricity consumption data and daily indicating value data, and the data cleaning of the daily electricity consumption data in the historical data includes the steps of:
judging whether the daily electricity quantity data has missing data within 30 consecutive days;
if the missing data exists, marking the missing data, acquiring the current date indicating data, judging whether the current date indicating data is missing,
if the daily indicating value data is missing, calculating the average value of the daily electricity consumption data of the days adjacent to the daily electricity consumption data,
recording the average value at a mark, supplementing the missing data, returning to the step of judging whether the daily electric quantity data has the missing data, and continuously executing the step, wherein the calculation formula is as follows:
R ti ＝A h＝24 -A h＝0
wherein R is ti Data representing daily electric power of the ith sewage treatment plant on the t day, A h＝24 Indicating the display data at 24 hours on day t, A h＝0 The data are the indication data at 0 h on the t day;
if the daily indicating value data is not missing, calculating the difference value of the daily indicating value data, recording the difference value at a mark, supplementing the missing data, returning to the step of judging whether the daily electricity consumption data in the 30 days has missing data and continuing to execute, wherein the calculation formula is as follows:
wherein R is ti Represents daily electricity quantity data of the ith sewage treatment plant on the t day, R (t-1)i The daily electricity consumption data, R, of the ith sewage treatment plant in the t-1 th day (t+1)i The daily electricity consumption data of the ith sewage treatment plant on the t +1 th day is represented;
and if the missing data does not exist, obtaining historical cleaning data.
The power utilization state of the sewage treatment plant in abnormal behaviors is mastered according to the daily power consumption data in the historical data, so that the situation is avoided, the fine monitoring level is improved, the daily power consumption data is subjected to data cleaning, the daily power consumption data is corrected and supplemented, the precision of the index weight coefficient is improved, and the accuracy of a judgment result is improved.
In a specific embodiment, before calculating the weight coefficient of each index in the historical cleaning data, the data of the actual treatment scale and the data of the annual total sewage treatment amount need to be corrected or supplemented, and the method comprises the following steps:
judging whether the total amount of the annual sewage treatment and the actual treatment scale meet the conditions, wherein the conditions are as follows:
S i ×365＜W i x 0.8 or S i ×365＞W i ×1.2
Wherein S is i Data showing the actual treatment scale of the ith sewage treatment plant, W i Represents the annual total sewage treatment data of the ith sewage treatment plant;
if S i And W i If the solution meets the above conditions, the method is based on
Modifying the actual processing scale data;
if S i Or W i If no solution is available, if the above condition is not satisfied, then
And supplementing the corresponding actual treatment scale data or annual total sewage treatment data.
Theoretically, the actual treatment scale data of the sewage treatment plant and the annual total sewage treatment data should meet the requirements
However, in the actual sewage treatment plant, due to the problem of non-correspondence between two parameters caused by external factors, the actual treatment scale data and the annual sewage treatment total amount data are corrected and supplemented, so that the precision of the index weight coefficient is improved, and the accuracy of the judgment result is further improved.
In a specific embodiment, the importance of the factor affecting the behavior anomaly of the pollution treatment plant is analyzed by using a Fuzzy Analytic Hierarchy Process (FAHP), and the relative membership degree is calculated according to the weight coefficient of each index, wherein the specific formula is as follows:
wherein,
wherein u is j Is a relative degree of membership, B p Is a feature matrix, w i Is the weight coefficient of the i-th index, A u To determine the matrix, a ij Is the relative value of the ith index to the jth index, and the value range is [1,9 ]]And reciprocal thereof, b ij Is the score of the j index to the i index, r ij Is the relative degree of membership of the jth index to the ith index.
And (4) according to the relative membership degree, carrying out weight sorting on each index, and selecting the first three most important indexes, namely the index with a larger weight coefficient as a behavior index.
In a specific embodiment, the behavior anomaly determination model includes one or more anomaly determinations, and includes the following steps:
judging whether the daily electricity data in the sewage treatment plant is 0 for all 30 continuous days;
if all the continuous 30 days are 0, judging that the daily electric quantity is abnormal by the sewage treatment plant;
if the total number of the continuous 30 days is not 0, judging that the daily electric quantity is normal by the sewage treatment plant;
calculating the ton water power consumption of the sewage treatment plant according to the actual treatment scale data and daily electricity consumption data every day by using the following calculation formula:
H i ＝R i /S i
wherein H i Represents the ton water power consumption of the ith sewage treatment plant, R i Represents daily electricity consumption data of the ith sewage treatment plant, S i Data indicating actual treatment scale of the ith sewage treatment plant;
dividing the sewage treatment plants into a plurality of categories according to actual treatment scale, and calculating the average value of the ton water power consumption of all the sewage treatment plants under each category;
judging whether the ton water power consumption of the sewage treatment plant under the category is less than 0.8 time of the average value of the ton water power consumption continuously for 7 days;
if so, judging that the power consumption per ton of water is abnormal by the sewage treatment plant;
if not, the power consumption per ton of water of the sewage treatment plant is normal;
calculating the variation coefficient of the daily electricity consumption data of the sewage treatment plant, and judging whether the variation coefficient is greater than 0.36, wherein the calculation formula is as follows:
wherein, cv i The coefficient of variation of daily electricity consumption data of the ith sewage treatment plant is shown, T represents the total time (days), R ti Represents daily electricity quantity data of the ith sewage treatment plant on the t day, mu i Expressing the arithmetic average value of the daily electricity consumption of the ith sewage treatment plant;
if the current power consumption is larger than the preset power consumption, judging that the daily power consumption fluctuation is abnormal by the sewage treatment plant;
if the power consumption is not more than the preset value, the daily power fluctuation of the sewage treatment plant is normal.
In a specific embodiment, the determination result includes one or more of abnormality of daily power consumption, abnormality of ton water power consumption, and abnormality of daily power fluctuation;
the judgment result comprises three abnormal behavior levels, and the abnormal daily electricity consumption is a first-level abnormal behavior;
if the power consumption per ton of water is abnormal and the daily power fluctuation is abnormal, the second-level behavior is abnormal;
and if any one of the abnormality of the power consumption per ton of water and the abnormality of the daily power fluctuation is abnormal, the three-level behavior is abnormal.
In a specific embodiment, the behavior abnormity level of the sewage treatment plant is judged, and the behavior abnormity judgment reliability is calculated according to the behavior abnormity level of the sewage treatment plant;
if the sewage treatment plant is abnormal in primary behavior, the reliability of judging the abnormal behavior is 100 percent;
if the sewage treatment plant is abnormal in secondary behaviors, the calculation formula of judging the reliability of the abnormal behaviors is as follows:
if the sewage treatment plant is abnormal in third-level behaviors, the calculation formula of judging the reliability of the abnormal behaviors is as follows:
wherein, P Ti Represents the abnormal judgment reliability, SR, of the ith sewage treatment plant in the statistical time period T Ti Shows the equilibrium value H when the actual treatment scale and the daily electricity quantity of the ith sewage treatment plant reach the equilibrium state within the statistical time period T Ti Represents the ton water power consumption H of the ith sewage treatment plant in the statistical time period T mean Means of the ton water power consumption, cv, under this category Ti And the variation coefficient of the daily electricity consumption data of the ith sewage treatment plant in the statistical time period T is shown.
The reliability of the behavior abnormity judgment adopts a corresponding calculation mode according to different grades of the behavior abnormity, so that the accuracy of the judgment result is improved, and a reference is provided for the efficient operation of the sewage treatment plant.
Example 2
the system comprises an acquisition unit 1, a monitoring unit and a monitoring unit, wherein the acquisition unit is used for acquiring historical data in a sewage treatment plant to be monitored;
a processing unit 2 connected with the collecting unit 1,
the historical data is subjected to data cleaning to obtain historical cleaning data;
calculating the weight coefficient of each index in the historical cleaning data, and selecting three indexes with the maximum weight coefficient as behavior indexes;
constructing a behavior abnormity judgment model according to the behavior indexes;
establishing a reliability index of the abnormal behavior judgment according to the judgment result of the abnormal behavior judgment model to obtain the reliability of the abnormal behavior judgment;
and the output unit 3 is connected with the processing unit 2 and is used for outputting the judgment reliability of the abnormal behavior of the sewage treatment plant to be monitored.
The actual conditions of the sewage treatment plant are collected through the collecting unit 1, importance analysis is carried out on all indexes in the processing unit 2 according to collected data, the index which has a large influence on the abnormal behavior of the sewage treatment plant is selected as a behavior index to establish a behavior abnormity judgment model, the behavior abnormity judgment reliability of the judgment result is calculated according to the behavior abnormity grade of the judgment result, and the accuracy of the behavior abnormity judgment of the sewage treatment plant is improved.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. The sewage treatment plant behavior abnormity judgment method based on electricity utilization data is characterized by comprising the following steps of:
acquiring historical data in a sewage treatment plant to be monitored, and performing data cleaning on the historical data to obtain historical cleaning data;
calculating the weight coefficient of each index in the historical cleaning data, and selecting three indexes with the maximum weight coefficients as behavior indexes;
constructing a behavior abnormity judgment model according to the behavior indexes;
and establishing a reliability index of the behavior abnormity judgment according to the judgment result of the behavior abnormity judgment model to obtain the reliability of the behavior abnormity judgment.
2. The method for judging the behavioral abnormality of the sewage treatment plant according to the electricity consumption data as claimed in claim 1, wherein the indexes include actual treatment scale, annual total sewage treatment amount, sewage treatment process, annual sewage treatment rate, design treatment scale, accumulated finished sewage pipe network length, discharge standard and construction operation state.
3. The method for determining the abnormal behavior of the sewage treatment plant based on the electricity consumption data as claimed in claim 2, wherein the historical data comprises daily electricity consumption data and daily indicating value data, and the step of performing data cleaning on the daily electricity consumption data in the historical data comprises the following steps:
judging whether the daily electric quantity data has missing data or not;
if the missing data exists, marking the missing data, acquiring the current date indicating data, judging whether the current date indicating data is missing or not,
if the daily indicating value data is missing, calculating the average value of the daily electricity quantity data of days adjacent to the daily electricity quantity data, recording the average value at a mark, supplementing the missing data, and returning to the step of judging whether the daily electricity quantity data has missing data for continuous execution;
if the daily indicating value data is not missing, calculating the difference value of the daily indicating value data, recording the difference value at a mark, supplementing the missing data, and returning to the step of judging whether the daily electric quantity data has the missing data for continuous execution;
and if the missing data does not exist, obtaining historical cleaning data.
4. The method for determining the behavioral abnormality of the sewage treatment plant according to the electricity consumption data of claim 3, wherein the data on the actual scale of the treatment and the data on the total annual sewage treatment amount are corrected or supplemented before the weight coefficient of each index in the historical cleaning data is calculated, and the method comprises the steps of:
judging whether the total annual sewage treatment amount and the actual treatment scale meet the conditions, wherein the conditions are as follows:
S i ×365＜W i x0.8 or S i ×365＞W i ×1.2
Wherein S is i Data showing the actual treatment scale of the i-th sewage treatment plant, W i Represents the annual total sewage treatment data of the ith sewage treatment plant;
if S i And W i If the solution meets the condition, then according to
Modifying the actual process size data;
if S i Or W i If no solution is available, the condition is not satisfied, then according to
And supplementing corresponding actual treatment scale data or annual total sewage treatment amount data.
5. The method for judging the behavioral abnormality of the sewage treatment plant based on the electricity consumption data according to claim 1, wherein the relative membership degree is calculated according to the weight coefficient of each index, the indexes are subjected to weight sorting, and three indexes with larger weight coefficients are selected as the behavioral indexes.
6. The method for judging the behavioral abnormality of the sewage treatment plant based on the electricity consumption data according to claim 5, wherein the relative membership degree is calculated according to the weight coefficient of each index, and the specific formula is as follows:
wherein,
wherein u is j Is a relative degree of membership, B p Is a feature matrix, w i Is the weight coefficient of the i-th index, A u To determine the matrix, a ij Is the relative value of the ith index to the jth index, and the value range is [1,9 ]]And reciprocal thereof, b ij Is the score of the j index to the i index, r ij Is the relative degree of membership of the jth index to the ith index.
7. The wastewater treatment plant behavior abnormality determination method based on electricity consumption data according to claim 4, characterized in that the behavior abnormality determination model includes one or more abnormality determinations, including the steps of:
judging whether all the daily electricity data in the sewage treatment plant is 0;
if all the electricity consumption values are 0, judging that the daily electricity consumption is abnormal by the sewage treatment plant;
if not all the daily electric quantity is 0, judging that the daily electric quantity is normal by the sewage treatment plant;
calculating the ton water power consumption of the sewage treatment plant according to the daily actual treatment scale data and daily electricity consumption data;
dividing the sewage treatment plants into a plurality of categories according to actual treatment scale, and calculating the average value of the ton water power consumption of all the sewage treatment plants under each category;
judging whether the ton water power consumption of the sewage treatment plant under the category is continuously less than the average value of 0.8 time of the ton water power consumption;
if yes, the sewage treatment plant judges that the power consumption per ton of water is abnormal;
if not, the power consumption per ton of water of the sewage treatment plant is normal;
calculating the variation coefficient of the daily electricity quantity data of the sewage treatment plant, and judging whether the variation coefficient is greater than 0.36;
if the current power consumption is larger than the preset power consumption, judging that the daily power consumption fluctuation is abnormal by the sewage treatment plant;
if the power consumption is not more than the preset value, the daily power fluctuation of the sewage treatment plant is normal.
8. The sewage treatment plant behavior abnormality determination method based on electricity consumption data according to claim 7, wherein the determination result includes one or more of abnormality of daily electricity consumption, abnormality of ton water electricity consumption, and abnormality of daily electricity fluctuation;
the judgment result comprises three behavioral anomaly levels, and the daily electricity consumption anomaly is a primary behavioral anomaly;
the abnormal electricity consumption per ton of water and the abnormal daily electricity fluctuation are both abnormal, and the abnormal second-level behavior is determined;
and if any one of the abnormality of the power consumption per ton of water and the abnormality of the daily power fluctuation is abnormal, the three-level behavior is abnormal.
9. The method for judging the behavioral abnormality of the sewage treatment plant based on the electricity consumption data according to claim 8, wherein the behavioral abnormality level of the sewage treatment plant is judged, and the behavioral abnormality judgment reliability is calculated according to the behavioral abnormality level of the sewage treatment plant;
if the sewage treatment plant is abnormal in primary behavior, the reliability of judging the abnormal behavior is 100 percent;
if the sewage treatment plant is abnormal in secondary behaviors, the calculation formula of judging the reliability of the abnormal behaviors is as follows:
if the sewage treatment plant is abnormal in third-level behaviors, the calculation formula of judging the credibility of the abnormal behaviors is as follows:
wherein, P Ti Represents the abnormal judgment reliability, SR, of the ith sewage treatment plant in the statistical time period T Ti Shows the equilibrium value H when the actual treatment scale and the daily electricity quantity of the ith sewage treatment plant reach the equilibrium state within the statistical time period T Ti Represents the ton water power consumption H of the ith sewage treatment plant in the statistical time period T mean Means of the ton water power consumption, cv, under this category Ti And the variation coefficient of the daily electricity consumption data of the ith sewage treatment plant in the statistical time period T is shown.
10. The system for judging the abnormal behavior of the sewage treatment plant based on the electricity consumption data is characterized by being used for realizing the method for judging the abnormal behavior of the sewage treatment plant based on the electricity consumption data according to any one of claims 1 to 9, and comprising the following steps:
the system comprises a collecting unit (1) and a monitoring unit, wherein the collecting unit is used for obtaining historical data in a sewage treatment plant to be monitored;
a processing unit (2),
the historical data is subjected to data cleaning to obtain historical cleaning data;
calculating the weight coefficient of each index in the historical cleaning data, and selecting the three indexes with the maximum weight coefficients as behavior indexes;
constructing a behavior abnormity judgment model according to the behavior indexes;
establishing a reliability index of the behavior abnormity judgment according to the judgment result of the behavior abnormity judgment model to obtain the reliability of the behavior abnormity judgment;
and the output unit (3) is used for outputting the judgment reliability of the abnormal behavior of the sewage treatment plant to be monitored.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115790723A (en) * | 2023-02-06 | 2023-03-14 | 山东中都机器有限公司 | Sewage purification abnormity detection method |
| CN118411085A (en) * | 2024-07-02 | 2024-07-30 | 台州市污染防治技术中心有限公司 | Emergency response system and method for illegal sewage discharge |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115790723A (en) * | 2023-02-06 | 2023-03-14 | 山东中都机器有限公司 | Sewage purification abnormity detection method |
| CN115790723B (en) * | 2023-02-06 | 2023-06-06 | 山东中都机器有限公司 | Sewage purification abnormality detection method |
| CN118411085A (en) * | 2024-07-02 | 2024-07-30 | 台州市污染防治技术中心有限公司 | Emergency response system and method for illegal sewage discharge |
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