CN114612266A - Urban pipe network drinking water quality monitoring and alarming system and method based on genetic algorithm - Google Patents
Urban pipe network drinking water quality monitoring and alarming system and method based on genetic algorithm Download PDFInfo
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Abstract
The invention discloses a city pipe network drinking water quality monitoring and alarming system and method based on genetic algorithm, wherein the method comprises the following steps: s1, dividing urban water supply network areas, and monitoring drinking water quality data in units; s2, collecting water quality data, and transmitting the water quality data to a data processing center for classification and storage; and S3, transmitting the data to a data analysis cloud platform based on a genetic algorithm, calculating a water quality safety correlation coefficient by using a pipe network water quality monitoring model based on the genetic algorithm, determining the abnormal condition of the pipe network water quality, and finishing water quality monitoring and alarming. The invention can monitor the water quality change condition of the urban drinking water pipe network in real time, based on the genetic algorithm, calculate the water quality safety correlation coefficient aiming at the water quality data measured in real time in the pipeline, determine the abnormal water quality condition of the pipe network according to the calculation result, if the maximum allowable water quality safety correlation coefficient is exceeded, judge that the water quality of the pipeline is polluted, and the system positions the polluted pipe section and gives an alarm.
Description
Technical Field
The invention relates to the technical field of urban pipe network water environment management, in particular to a system and a method for monitoring and alarming urban pipe network drinking water quality based on a genetic algorithm.
Background
Water resources are indispensable natural resources for human beings to live, however, along with the acceleration of the urbanization process, the water resources are polluted more and more, and the quality of drinking water for human beings is difficult to guarantee. Therefore, the problem of monitoring the quality of the drinking water is particularly important.
Drinking water passes through the water supply network and carries thousands of households, and the drinking water is probably producing water pollution because rivers blend, pipeline ageing scheduling problem shuttling around in the in-process of water supply network. In recent years, water pollution caused in the water supply link is becoming more and more difficult to ignore. Displaying according to one item of data: at present, a plurality of places in a city water supply network in China are at the critical point of service life, and the old city pipe network of part of cities runs out of term. The natural water pipe network in many old cities is made of cast iron, and after long-time use, the inner wall is easy to rust, scale and fall off, so that the water quality is polluted, and the normal drinking health of people is influenced.
In water environment research, the conditions of water flow and water quality of a water body need to be known, and the current common methods for researching water flow and water quality comprise field observation and physical model tests. The on-site observation can provide original data for the physical model test, is a main basis for checking whether the physical model test is successful or not, and certainly needs certain manpower, material resources and financial support. The physical model is restricted by the similarity rate, the test period is long, the natural wind-generated circulation and the biochemical process of substances of a water body system are difficult to simulate, and the change of the real environmental condition is difficult to simulate.
The existing method for controlling water quality pollution in the water supply process mainly adopts manual monitoring, water quality parameters are measured at monitoring points at fixed time and fixed points, water environment change cannot be monitored in real time, and the measured data has no strong persuasion due to uneven distribution and insufficient quantity of the monitoring points, so that the water quality abnormality of a pipe network is difficult to find in time under the condition of low efficiency of treating pipeline pollution.
Disclosure of Invention
The invention provides a city pipe network drinking water quality monitoring alarm system and method based on a genetic algorithm, which aim to solve the water quality pollution condition of a water supply pipe network and solve the problems of insufficient monitoring points and difficult positioning of water quality change pipe sections during drinking water monitoring.
The technical scheme of the invention is realized as follows:
city pipe network drinking water quality monitoring alarm system based on genetic algorithm includes:
the region division module is used for dividing urban water supply network regions and monitoring drinking water quality data in units;
the data acquisition module is used for acquiring the water quality data transmitted by the region division module and generating a water quality parameter signal;
the data processing center is used for carrying out analog-to-digital conversion on the water quality parameter signals, and carrying out data classification and storage;
the data analysis cloud platform based on the genetic algorithm receives data transmitted by the data processing center, a pipe network water quality monitoring model based on the genetic algorithm is built in the data analysis cloud platform, and the water quality pollution degree and position are judged according to the water quality safety correlation coefficient of each index of the actual measurement point;
and the alarm module is used for receiving the data transmitted by the data analysis cloud platform based on the genetic algorithm and giving an alarm when the water quality is abnormal.
Further optimize technical scheme, the data acquisition module includes:
the power supply module is used for providing a stable power supply;
the data import module is used for importing the parameter data information of the pipelines in batches;
the transmission interface is connected with the water quality parameter sensor and is used for signal transmission;
and the water quality parameter acquisition module is connected with the transmission interface and used for monitoring various water quality parameters in the pipeline in real time and generating a water quality parameter signal.
Further optimize technical scheme, data processing center includes:
the data receiving unit is used for receiving the water quality parameter signal;
the signal conversion module is used for carrying out analog-to-digital conversion on the water quality parameter signal;
the data preprocessing unit is used for classifying the data after analog-to-digital conversion;
and the data storage center is used for storing the data after the analog-to-digital conversion.
Further optimizing the technical scheme, the expression of the water quality safety correlation coefficient is as follows:
in the formula: a. thenaisThe content of the water quality parameter a measured at the s time at the point i of the nth unit in the front end A is represented; a. thenbisThe content of the water quality parameter b measured at the s time at the point i of the nth unit in the front end A is represented; r is a water quality safety correlation coefficient;
the expression of the water quality safety correlation coefficient is popularized to multiple indexes to calculate the water quality safety correlation coefficient, and the following steps are introduced:
in the formula: a. thenaisThe content of the water quality parameter a measured at the s time at the point i of the nth unit in the front end A is represented; a. thenbisThe content of the water quality parameter b measured at the s time at the point i of the nth unit in the front end A is represented; a. thencisShowing the content of the water quality parameter c measured at the s-th time at the point i of the nth unit in the front end A; a. thenmisShowing the content of the water quality parameter m measured at the s time at the point i of the nth unit in the front end A; r is a water quality safety correlation coefficient;
if the absolute value of the water quality safety correlation coefficient is less than or equal to 1, the water quality parameter is normal; and if the absolute value of the water quality safety correlation coefficient is greater than 1, the water quality parameter is abnormal.
The city pipe network drinking water quality monitoring and alarming method based on the genetic algorithm is characterized in that the method is carried out based on the city pipe network drinking water quality monitoring and alarming system based on the genetic algorithm, and comprises the following steps:
s1, dividing urban water supply network areas, and monitoring drinking water quality data in units;
s2, collecting water quality data, and transmitting the water quality data to a data processing center for classification and storage;
and S3, transmitting the data to a data analysis cloud platform based on a genetic algorithm, calculating a water quality safety correlation coefficient by using a pipe network water quality monitoring model based on the genetic algorithm, determining the abnormal condition of the pipe network water quality, and finishing water quality monitoring and alarming.
Further optimizing the technical solution, the step S1 includes the following steps:
s11, in the area division module, dividing the position sequence from the water plant to the user into three large areas of a front end, a middle end and a tail end according to the principle of balancing the scale of each partition, taking the important pipeline junction as a partition boundary line, and respectively representing the large areas as the front end A, the middle end B and the tail end C;
and S12, continuously dividing the cell area of the front middle tail end and the middle tail end, and arranging a single-parameter sensor or a multi-parameter sensor on a cell junction.
Further optimizing the technical scheme, the establishment of the pipe network water quality monitoring model based on the genetic algorithm comprises the following steps:
A. establishing a water quality model of a target pipe network;
B. partitioning units according to a region partitioning module, creating an initial population, and coding;
C. importing the data into a monitoring point location of a region division module, importing the data measured in real time by the region division module into a water quality model of a target pipe network, and calculating a water quality safety correlation coefficient by using a genetic algorithm;
D. and (3) establishing a pipe network water quality monitoring model based on a genetic algorithm, and judging the water quality pollution degree and position according to the water quality safety correlation coefficient of each index of the actual measurement point position.
By adopting the technical scheme, the invention has the beneficial effects that:
the invention can monitor the water quality change condition of the urban drinking water pipe network in real time, based on the genetic algorithm, calculate the water quality safety correlation coefficient aiming at the water quality data measured in real time in the pipeline, determine the abnormal water quality condition of the pipe network according to the calculation result, if the maximum allowable water quality safety correlation coefficient is exceeded, judge that the water quality of the pipeline is polluted, and the system positions the polluted pipe section and gives an alarm.
The invention combines an intelligent optimization algorithm, utilizes a genetic algorithm to simulate a new population generated by a genetic mechanism in nature, and repeats the cycle operation until a termination condition is met to obtain the optimal individual in the population, namely, the water quality safety correlation coefficient is calculated, and the real-time water quality condition of a pipe network can be more comprehensively and effectively monitored by utilizing the genetic algorithm, thereby improving the water quality monitoring efficiency.
The urban water supply pipe network is reasonably partitioned, the unified scheduling management of the urban pipe network is optimized through intelligent regional information classification, and the accuracy of water pollution positioning is improved.
The invention provides a concept of water quality safety correlation coefficient, judges the real-time condition of water quality by utilizing the correlation among water quality parameters, and improves the accuracy and precision of the water quality pollution condition.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic block diagram of an urban pipe network drinking water quality monitoring alarm system based on a genetic algorithm;
FIG. 2 is a schematic block diagram of a data acquisition module in the urban pipe network drinking water quality monitoring and alarming system based on genetic algorithm;
FIG. 3 is a schematic diagram of the principle of a pipe network water quality monitoring model based on a genetic algorithm;
FIG. 4 is a schematic view of a "well" grid of cells in a zone partitioning module according to the present invention.
Wherein: 10. the system comprises a region division module, 20, a data acquisition module, 21, a power supply module, 22, a data import module, 23, a transmission interface, 24, a water quality parameter acquisition module, 30, a data processing center, 31, a data receiving unit, 32, a data preprocessing unit, 33, a data storage center, 40, a data analysis cloud platform based on a genetic algorithm, 50 and an alarm module.
Detailed Description
The technical solutions of the present invention will be clearly and completely described below in connection with specific embodiments, but it should be understood by those skilled in the art that the embodiments described below are only for illustrating the present invention and should not be construed as limiting the scope of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The city pipe network drinking water quality monitoring and alarming system based on the genetic algorithm comprises an area division module 10, a data acquisition module 20, a data processing center 30, a data analysis cloud platform 40 based on the genetic algorithm and an alarming module 50. The output end of the region dividing module 10 is connected to the input end of the data acquisition module 20, the output end of the data acquisition module 20 is connected to the input end of the data processing center 30, the output end of the data processing center 30 is connected to the input end of the data analysis cloud platform 40 based on the genetic algorithm, and the output end of the data analysis cloud platform 40 based on the genetic algorithm is connected to the input end of the alarm module 50.
The regional division module 10 is used for dividing the urban water supply network region and monitoring the drinking water quality data in units. The area division module 10 comprises a water quality parameter sensor installed on a pipeline of a partition unit, and the water quality condition is monitored in real time through the water quality parameter sensor.
And the data acquisition module 20 is used for acquiring the water quality data transmitted by the region division module 10 and generating a water quality parameter signal. The data acquisition module 20 comprises a power module 21, a data import module 22, a transmission interface 23 and a water quality parameter acquisition module 24.
And the power supply module 21 is used for providing stable power supply.
And the data import module 22 is connected with the transmission interface 23 and is used for importing parameter data information of the pipeline in batches, wherein the parameter data information of the pipeline comprises roughness, pipe diameter, position, material, connection mode of each pipe section, sensor equipment parameter data and other information.
And the input end of the transmission interface 23 is connected with the water quality parameter sensor, and the output end of the transmission interface is connected with the data import module 22 and used for signal transmission.
And the water quality parameter acquisition module 24 is connected with the transmission interface and used for monitoring various water quality parameters in the pipeline in real time and generating water quality parameter signals. The water quality parameters mainly comprise parameters such as residual chlorine, pH value, turbidity, oxygen content and the like.
And the data processing center 30 is used for performing analog-to-digital conversion on the water quality parameter signals, and performing data classification and storage. The data processing center 30 includes a data receiving unit 31, a signal conversion module, a data preprocessing unit 32 and a data storage center 33, which are respectively used for receiving, classifying and storing data.
A data receiving unit 31, configured to receive the water quality parameter signal.
And the signal conversion module is used for performing analog-to-digital conversion on the water quality parameter signal.
And the data preprocessing unit 32 is used for performing classification processing on the data after analog-to-digital conversion.
And the data storage center 33 is used for storing the data after analog-to-digital conversion.
The data analysis cloud platform 40 based on the genetic algorithm has an input end connected to an output end of the data processing center 30, and receives data transmitted by the data processing center 30. A pipe network water quality monitoring model based on a genetic algorithm is built in the data analysis cloud platform 40 based on the genetic algorithm, and the water quality pollution degree and position are judged according to the water quality safety correlation coefficient of each index of the actual measurement point.
And the alarm module 50 is used for receiving the data transmitted by the data analysis cloud platform 40 based on the genetic algorithm and giving an alarm when the water quality is abnormal. And after the water quality safety related coefficient exceeds the set maximum allowable value, starting an alarm signal and transmitting the alarm signal to the alarm module 50, wherein after the alarm module 50 receives the alarm signal, the alarm signal parameter is sent to an alarm device to control actions including but not limited to lighting of an alarm lamp near a target pipeline, flashing of an alarm mark on a display interface and the like.
Taking the monitoring of the quality of drinking water in a water supply network in a certain mountain area in a certain city as an example, the method for monitoring and alarming the quality of drinking water in the urban pipe network based on the genetic algorithm comprises the following steps:
and S1, dividing the water supply pipe network area of a certain mountain area, taking the turning point of an important main pipe as a boundary, and dividing the area of the pipe network of each area according to the dividing principle, so that the planning and management are convenient, and no distance limitation exists between the areas. The drinking water quality data is monitored by units, and the water quality safety is comprehensively guaranteed.
Step S1 includes the following steps:
s11, in the area dividing module 10, based on the principle of balancing the scale of each partition, taking the important pipeline junction or turning point as the boundary line of the partition, dividing the position from the water plant to the user into three large areas, front end, middle end and tail end, which are respectively denoted as front end a, middle end B and tail end C. The area of each area pipe network is uniform, and planning and management are facilitated.
And S12, continuously dividing the front middle tail end into small units, wherein each unit is in a grid shape of a well and comprises 4 to 6 pipe water flow turning points, and two to three turning points are provided with a single-parameter sensor or a multi-parameter sensor for monitoring water quality data, such as a residual chlorine sensor, a dissolved oxygen sensor, a conductivity sensor, an ORP sensor and the like.
And S2, collecting water quality data parameters, and transmitting the water quality data to the data processing center 30 for classification and storage. The water quality data parameters comprise residual chlorine, pH value, turbidity, oxygen content and the like.
Step S2 includes the following steps:
and S21, collecting water quality data of each unit. The data import module 22 collects the roughness, the pipe diameter, the position, the material, the connection mode and the parameter data of the sensor equipment of each pipe section; the water quality parameter acquisition module 24 monitors various water quality parameters in the pipeline in real time to generate water quality parameter signals.
And S22, measuring data once per hour by two measuring points, converging the data into a unit group, allowing the unit group data to pass through a data receiving unit, entering a data preprocessing center for primary classification processing, storing the data in the data center for archiving, and transmitting the data 24 times every day.
S23, preprocessing the data by the data processing center, and numbering and classifying the data: front end A, data transmission for the 1 st time, data transmission for the 2 nd time, … … rd time (s is more than or equal to 1 and less than or equal to 24) for the 2 rd time and the 3 rd time, various water quality and variety data (PH value a, ion content such as copper b, nickel c and chromium d, oxygen content percentage e … …), unit labels (A) in the front end A1、A2、A3……An) The two monitoring points in each unit are labeled i and ii. The content of the kind index measured at the i-th point of the nth cell in the front end A at the s-th time can be expressed as Anais. The middle and end data are classified and so on.
And S3, transmitting the data to the data analysis cloud platform 40 based on the genetic algorithm, calculating a water quality safety correlation coefficient by using a pipe network water quality monitoring model based on the genetic algorithm, determining the abnormal condition of the pipe network water quality, and finishing water quality monitoring and alarming.
The establishment of the pipe network water quality monitoring model based on the genetic algorithm comprises the following steps:
A. and establishing a water quality model of the target pipe network.
B. The partitioning module 10 performs partitioning unit according to the region, creates an initial population, and performs encoding, where the specific encoding principle may be referred to in step S23.
C. The water quality monitoring sensor is led into the monitoring point position of the water quality monitoring sensor in the region dividing module 10, data measured in real time by the water quality monitoring sensor is led into a water quality model of a target pipe network, a new population generated by a genetic mechanism in nature is simulated by using a genetic algorithm, the circulating operation is repeated until a termination condition is met, the optimal individual in the population is obtained, namely, the water quality safety correlation coefficient is calculated, and a water pollution database is established.
D. And (3) establishing a pipe network water quality monitoring model based on a genetic algorithm, and judging the water quality pollution degree and position according to the water quality safety correlation coefficient of each index of the actual measurement point position.
And (3) the termination condition is that the maximum allowable value of the water quality safety correlation coefficient is exceeded, the precision reaches 0.01, the iteration frequency does not exceed 30 times, the cycle operation is repeated until the occurrence of the global optimal solution is met, and the iteration is ended.
When a single water quality parameter at a certain position in a pipe network is abnormal, the change amplitude of a single index is not large, so that the judgment is difficult, but the abnormal of one index can drive other indexes of the water quality at the position to change, when a plurality of indexes are abnormal almost at the same time, the water quality at the position can be judged whether to be abnormal, so that correlation coefficients are established among a plurality of indexes, and the judgment can be timely and accurately made by utilizing a genetic algorithm through the correlation coefficient values. Thereby leading out a water quality safety correlation coefficient, and the formula is as follows:
in the formula: a. thenaisThe content of the water quality parameter a measured at the s time at the point i of the nth unit in the front end A is represented; a. thenbisThe content of the water quality parameter b measured at the s time at the point i of the nth unit in the front end A is represented; r is the water quality safety correlation coefficient.
Anais、AnbisThe two water quality indexes are respectively, and the correlation of the two indexes can be obtained through the calculation of the water quality safety correlation coefficients of the two water quality indexes. If the absolute value is less than or equal to 1, the water quality parameter is normal; and if the absolute value of the water quality safety correlation coefficient is larger than 1, determining that the water quality is abnormal, and determining that the water quality is abnormal.
When water quality is polluted, not only one index changes, but also one or more other indexes change to an unappreciable degree due to the change of one index, so that the function is not limited to two indexes, and can be popularized to the multiple indexes to calculate the water quality safety correlation coefficient, thereby leading out:
in the formula: a. thenaisThe content of the water quality parameter a measured at the s time at the point i of the nth unit in the front end A is represented; a. thenbisThe content of the water quality parameter b measured at the s time at the point i of the nth unit in the front end A is represented; a. thencisShowing the content of the water quality parameter c measured at the s time at the point i of the nth unit in the front end A; a. thenmisShowing the content of the water quality parameter m measured at the s time at the point i of the nth unit in the front end A; r is the water quality safety correlation coefficient.
After the correlation coefficient of water quality safety is calculated by the correlation coefficient calculation module and exceeds the maximum allowable value, an alarm signal is sent out and transmitted to the alarm module 50, and after the alarm module 50 receives the alarm signal, an alarm signal parameter is sent to an alarm device to control actions including but not limited to lighting of an alarm lamp near a target pipeline, flashing of an alarm mark of a display interface and the like.
The water quality safety coefficient can also judge the water quality pollution degree, the larger the deviation between the value and the maximum allowable value is, the deeper the pollution degree is, and according to the water quality condition monitored by the sensor signal source, the partition unit where the point with the water quality problem is positioned can be judged and accurately positioned.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (8)
1. City pipe network drinking water quality monitoring alarm system based on genetic algorithm, its characterized in that includes:
the region division module is used for dividing urban water supply network regions and monitoring drinking water quality data in units;
the data acquisition module is used for acquiring the water quality data transmitted by the region division module and generating a water quality parameter signal;
the data processing center is used for carrying out analog-to-digital conversion on the water quality parameter signals and carrying out data classification and storage;
the data analysis cloud platform based on the genetic algorithm receives data transmitted by the data processing center, a pipe network water quality monitoring model based on the genetic algorithm is built in the data analysis cloud platform, and the water quality pollution degree and position are judged according to the water quality safety correlation coefficient of each index of the actual measurement point;
and the alarm module is used for receiving the data transmitted by the data analysis cloud platform based on the genetic algorithm and giving an alarm when the water quality is abnormal.
2. The city pipe network drinking water quality monitoring and alarming system based on the genetic algorithm as claimed in claim 1, wherein the area division module comprises a water quality parameter sensor installed on a pipe of a division unit.
3. The city pipe network drinking water quality monitoring and alarming system based on genetic algorithm as claimed in claim 2, wherein the data acquisition module comprises:
the power supply module is used for providing a stable power supply;
the data import module is used for importing the parameter data information of the pipelines in batches;
the transmission interface is connected with the water quality parameter sensor and is used for signal transmission;
and the water quality parameter acquisition module is connected with the transmission interface and used for monitoring various water quality parameters in the pipeline in real time and generating a water quality parameter signal.
4. The city pipe network drinking water quality monitoring and alarming system based on genetic algorithm as claimed in claim 1, wherein the data processing center comprises:
the data receiving unit is used for receiving the water quality parameter signal;
the signal conversion module is used for carrying out analog-to-digital conversion on the water quality parameter signal;
the data preprocessing unit is used for classifying the data after analog-to-digital conversion;
and the data storage center is used for storing the data after the analog-to-digital conversion.
5. The city pipe network drinking water quality monitoring and alarming system based on the genetic algorithm as claimed in claim 1, wherein the expression of the water quality safety correlation coefficient is as follows:
in the formula: a. thenaisRepresenting the content of the water quality parameter a measured at the s time at the point i of the nth unit in the front end A; a. thenbisThe content of the water quality parameter b measured at the s time at the point i of the nth unit in the front end A is represented; r is a water quality safety correlation coefficient;
the expression of the water quality safety correlation coefficient is popularized to multiple indexes to calculate the water quality safety correlation coefficient, and the following steps are introduced:
in the formula: a. thenaisThe content of the water quality parameter a measured at the s time at the point i of the nth unit in the front end A is represented; a. thenbisRepresenting the content of the water quality parameter b measured at the s time at the point i of the nth unit in the front end A; a. thencisShowing the content of the water quality parameter c measured at the s time at the point i of the nth unit in the front end A; a. thenmisAn i measuring point of the nth unit of the water quality parameter m in the front end AThe content measured at the s-th time; r is a water quality safety correlation coefficient;
if the absolute value of the water quality safety correlation coefficient is less than or equal to 1, the water quality parameter is normal; and if the absolute value of the water quality safety correlation coefficient is greater than 1, the water quality parameter is abnormal.
6. The city pipe network drinking water quality monitoring and alarming method based on the genetic algorithm is characterized in that the method is carried out based on the city pipe network drinking water quality monitoring and alarming system based on the genetic algorithm according to any one of claims 1 to 5, and comprises the following steps:
s1, dividing urban water supply network areas, and monitoring drinking water quality data in units;
s2, collecting water quality data, and transmitting the water quality data to a data processing center for classification and storage;
and S3, transmitting the data to a data analysis cloud platform based on a genetic algorithm, calculating a water quality safety correlation coefficient by using a pipe network water quality monitoring model based on the genetic algorithm, determining the abnormal condition of the pipe network water quality, and finishing water quality monitoring and alarming.
7. The city pipe network drinking water quality monitoring and alarming method based on genetic algorithm of claim 6, wherein the step S1 comprises the following steps:
s11, in the area division module, dividing the position sequence from the water plant to the user into three large areas of a front end, a middle end and a tail end according to the principle of balancing the scale of each partition, taking the important pipeline junction as a partition boundary line, and respectively representing the large areas as the front end A, the middle end B and the tail end C;
and S12, continuously dividing the cell area of the front middle tail end and the middle tail end, and arranging a single-parameter sensor or a multi-parameter sensor on a cell junction.
8. The city pipe network drinking water quality monitoring and alarming method based on the genetic algorithm as claimed in claim 6, wherein the establishment of the pipe network water quality monitoring model based on the genetic algorithm comprises the following steps:
A. establishing a water quality model of a target pipe network;
B. partitioning units according to a region partitioning module, creating an initial population, and coding;
C. importing the data into a monitoring point location of a region division module, importing the data measured in real time by the region division module into a water quality model of a target pipe network, and calculating a water quality safety correlation coefficient by using a genetic algorithm;
D. and (3) establishing a pipe network water quality monitoring model based on a genetic algorithm, and judging the water quality pollution degree and position according to the water quality safety correlation coefficient of each index of the actual measurement point position.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112037106A (en) * | 2020-08-07 | 2020-12-04 | 汉威科技集团股份有限公司 | Data anomaly analysis method based on characteristic cross correlation and probability density |
| CN116466058A (en) * | 2023-06-15 | 2023-07-21 | 上海博取仪器有限公司 | Water quality detection data processing method, water quality evaluation system, equipment and medium |
| CN117805338A (en) * | 2024-03-01 | 2024-04-02 | 广东省建筑设计研究院有限公司 | Real-time on-line monitoring method and system for water quality of building water supply pipe network |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112037106A (en) * | 2020-08-07 | 2020-12-04 | 汉威科技集团股份有限公司 | Data anomaly analysis method based on characteristic cross correlation and probability density |
| CN112037106B (en) * | 2020-08-07 | 2023-12-15 | 汉威科技集团股份有限公司 | Data anomaly analysis method based on feature cross-correlation and probability density |
| CN116466058A (en) * | 2023-06-15 | 2023-07-21 | 上海博取仪器有限公司 | Water quality detection data processing method, water quality evaluation system, equipment and medium |
| CN116466058B (en) * | 2023-06-15 | 2023-09-05 | 上海博取仪器有限公司 | Water quality detection data processing method, water quality evaluation system, equipment and medium |
| CN117805338A (en) * | 2024-03-01 | 2024-04-02 | 广东省建筑设计研究院有限公司 | Real-time on-line monitoring method and system for water quality of building water supply pipe network |
| CN117805338B (en) * | 2024-03-01 | 2024-05-28 | 广东省建筑设计研究院有限公司 | Real-time on-line monitoring method and system for water quality of building water supply pipe network |
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