CN117670051A - Unmanned urban and rural water supply operation and maintenance analysis system - Google Patents
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Abstract
The invention relates to the technical field of water supply operation and maintenance, in particular to an unattended urban and rural water supply operation and maintenance analysis system, which comprises a water quality monitoring module, a water quality data analysis module, a water supply management module, a water quality risk assessment module, a user feedback module and a central data management unit, wherein the water quality monitoring module is connected with the water supply management module; wherein, water quality monitoring module: monitoring microbial activity, chemical content and changes thereof in the water in real time; the water quality data analysis module: predicting the water quality change trend and identifying potential pollution sources; and a water supply management module: according to the water quality data analysis result, adjusting a water supply strategy; a water quality risk assessment module; for assessing water quality related health and environmental risks; and a user feedback module: user feedback is collected regarding water quality and water supply services. The invention realizes automatic water quality monitoring, integrated data analysis and a user feedback mechanism, remarkably improves the efficiency, accuracy and response capability of urban and rural water supply systems, and enhances the user satisfaction.
Description
Technical Field
The invention relates to the technical field of water supply operation and maintenance, in particular to an unattended urban and rural water supply operation and maintenance analysis system.
Background
In modern urban and rural water supply systems, main challenges include the deficiency of water quality monitoring, the lag of water supply management strategies and the lack of user feedback mechanisms, and conventional water supply systems generally depend on manual monitoring and decentralized data processing methods, which are not only inefficient, but also fail to reflect water quality changes and user demands in real time, and in addition, lack of effective data integration and analysis tools, which results in the inability of water supply management decisions to respond to actual conditions in time, increasing the waste of water resources and the risk of water quality accidents.
With the development of information technology, although some urban and rural attempts are made to improve the efficiency of water supply systems by introducing automatic monitoring devices and computer-aided data processing, there are still a number of problems, for example, existing systems often lack effective data integration capability, so that key information cannot be efficiently shared among different modules, and in addition, these systems usually do not consider user feedback, neglect understanding of user experience and requirements, and thus affect the overall quality of water supply services.
Disclosure of Invention
Based on the above purpose, the invention provides an unattended urban and rural water supply operation and maintenance analysis system.
An unmanned urban and rural water supply operation and maintenance analysis system comprises a water quality monitoring module, a water quality data analysis module, a water supply management module, a water quality risk assessment module, a user feedback module and a central data management unit; wherein,
and the water quality monitoring module is: the method comprises the steps of adopting a biosensor and a chemical analysis technology to monitor the microbial activity, the chemical substance content and the change thereof in water in real time so as to detect the existence of trace pollutants;
the water quality data analysis module: deep analysis is carried out on the data collected from the water quality monitoring module so as to predict the water quality change trend and identify potential pollution sources;
and a water supply management module: according to the water quality data analysis result, adjusting a water supply strategy, including water source selection, water treatment process optimization and water supply network adjustment;
a water quality risk assessment module; for assessing water quality related health and environmental risks and for issuing an early warning when a potential dangerous level is detected;
and a user feedback module: collecting feedback of users on water quality and water supply service through smart phone application and an online platform;
a central data management unit: for coordinating the data operation and exchange of the various modules and communicating system-generated insights and suggestions to the operation and maintenance team and decision-makers.
Further, the water quality monitoring module comprises a microorganism detection unit, a chemical analysis unit and a pollutant identification unit; wherein,
microorganism detection unit: a fluorescence-marked biosensor is adopted for monitoring the activities of microorganisms in water in real time, and the biosensor detects the microorganisms through the biological markers and reflects the increase and decrease of the microorganism content through the change of fluorescence signals;
chemical analysis unit: determining the content and variation of chemical substances in water by using high performance liquid chromatography and mass spectrometry techniques, wherein the liquid chromatography is used for separating various compounds in a water sample, and the mass spectrometry is used for identifying and quantifying the compounds;
contaminant identification unit: the method is used for detecting trace pollutants in real time by combining infrared spectrum analysis and electrochemical sensing technology, wherein the infrared spectrum analysis can identify the spectrum characteristics of specific compounds, and the electrochemical sensor is used for detecting the concentration of heavy metals and organic pollutants in water.
Further, the water quality data analysis module comprises a data preprocessing unit, a trend analysis unit, an abnormality detection unit and a source tracking unit; wherein,
a data preprocessing unit: preprocessing the collected water quality data by adopting a standardized and normalized processing method, wherein the preprocessing comprises removing noise, filling missing values and normalizing the data so as to ensure the quality and consistency of the data;
trend analysis unit: performing time series analysis by applying an autoregressive moving average model (ARMA), and analyzing historical and real-time data of water quality data to predict the trend and mode of water quality change, wherein the ARMA model formula is as follows:
wherein->Is the observation of the time point, +.>Is a constant term->Is a white noise item, < >>And->The order of the autoregressive and moving average parts of the model, respectively, +.>And->Parameters of the autoregressive and moving average models, respectively;
an abnormality detection unit: identifying abnormal points in the data set by an abnormal value detection method of an isolation forest, wherein the abnormal points represent predicted potential pollution events, and the implementation steps of the isolation forest algorithm are as follows:
a. randomly selecting a feature;
b. randomly selecting a segmentation value of the feature;
c. repeating the steps a-b to construct a plurality of isolation trees to form a forest;
the anomaly score is based on path length, wherein the path length is the distance from the root node to the termination node, and the calculation formula of the anomaly score is as follows:
wherein->Is sample->Is (are) abnormality score>Is the sample size,/->Is sample->Average path length in all isolation trees, < >>Is a normalization factor for correcting the path length of different sized sample sets;
source tracking unit: the method comprises principal component analysis PCA and cluster analysis, and is used for identifying and tracking potential pollution sources.
Further, the step of identifying and tracking the potential pollution source in the source tracking unit specifically includes:
s1: collecting various water quality parameter data provided by a water quality monitoring module, including chemical substance content, heavy metal concentration and microorganism quantity;
s2: performing dimension reduction processing on the collected data by using a PCA method, wherein the PCA calculation formula is as follows:
wherein->Is the original data matrix, < >>Is a principal component weight matrix extracted from the data covariance matrix,/a>Is the principal component score matrix after conversion;
s3: performing cluster analysis on the data subjected to PCA dimension reduction, and performing minimization of the sum of squares in the cluster by using a K-means clustering algorithm, wherein the specific formula is as follows:
wherein->Is the number of clusters, +.>Is->Data point set in each cluster, +.>Is->Center point of each cluster,/>Is a data point;
s4: based on the clustering results, different potential pollution sources will be identified, wherein each cluster represents a potential pollution pattern, and the geographic location, time plume and chemical composition characteristics of the pollution sources will be used to track and identify the pollution sources.
Further, the water supply management module comprises a water source decision unit, a water treatment optimizing unit, a pipe network scheduling unit, a data feedback and adjustment unit and an emergency treatment unit; wherein,
the water source decision unit: analysis of contaminant content based on water quality dataAnd microbial Activity->Decision algorithm ∈>Selecting an optimal water source based on a preset threshold +.>And->Decision making is carried out, and a specific decision formula is as follows:
wherein, the water source->And->Respectively representing different water source options;
water treatment optimizing unit: according to the water quality monitoring resultAdjusting the water treatment process parameters->Specifically adjusting the dosage of the chemical treatment agent>The formula is:
wherein->Is an adjustment coefficient for adjusting the addition amount of the chemical treatment agent according to the water quality condition;
a pipe network scheduling unit: using hydraulic modelsOptimizing the pipe network, wherein->Flow rate->Is a water quality parameter, the model is obtained by optimizing the valve +.>And pump station->The water supply is adjusted by setting, and the specific formula is as follows:
；
the data feedback and adjustment unit: will actually supply waterFeedback to water quality data analysis module, using feedback adjustment algorithm +.>Continuously monitoring and adjusting a water supply strategy, the formula being expressed as:/>wherein->Is a feedback adjustment coefficient for adjusting the water supply strategy according to the actual situation;
an emergency processing unit: when the serious water quality problem S is detected, starting an emergency treatment process, and switching to a standby water source when the water quality problem exceeds a critical value; otherwise, the water treatment measures are enhanced.
Further, the water quality risk assessment module comprises a microorganism concentration analysis unit, a risk grade assessment unit and an early warning mechanism unit; wherein,
microorganism concentration analysis unit: the quantitative method is used for analyzing the concentration of specific chemical substances and microorganisms in a water sample, specifically, the spectral analysis technology is used for measuring the concentration of the specific chemical substances, and the concentration of the microorganisms is measured by a culture method or a molecular biological technology, and a specific calculation formula is shown as follows:
wherein->And->Conversion functions of chemical and microorganism concentration and detection response, respectively;
risk level evaluation unit: according to the concentration analysis result, the health and environmental risk level of the water quality is evaluated, and the safety threshold value of different pollutants is setAnd->The risk is classified into different grades according to the degree that the concentration exceeds a threshold value;
an early warning mechanism unit: when the water quality risk level reaches "high", an early warning mechanism is automatically triggered, including notifying the relevant management and emergency response team, and alerting the public if necessary.
Further, the risk level is three levels of low, medium and high, and the specific calculation distinguishing standard is as follows:
the proximity refers to a concentration range that approaches but does not exceed a safe interval value.
Further, the user feedback module comprises a feedback collection unit, a data processing unit, a feedback analysis unit and a feedback response unit; wherein,
feedback collection unit: collecting user feedback through smart phone application and an online platform, wherein the user directly reports water quality problems, water supply interruption or other water supply service related problems through the platform;
a data processing unit: classifying and primarily analyzing the collected feedback data, and extracting key information by using a text analysis technology, wherein the key information comprises the type, time, place and influence degree of the problem;
feedback analysis unit: based on the processed data, performing a deep analysis to identify common problems or trends, using statistical analysis and data mining techniques to find potential problems or user concerns;
feedback response unit: and (3) formulating and implementing a response strategy according to the analysis result, wherein the strategy comprises informing related departments to carry out emergency repair, adjusting a water supply operation and maintenance plan or providing necessary information and guidance for users.
Furthermore, the central data management unit ensures real-time synchronization and consistency of data among different modules based on a data integration technology, wherein the data integration technology collects data of each module through a unified interface and protocol, and the data comprises water quality monitoring, user feedback and water management strategies.
The invention has the beneficial effects that:
according to the invention, by introducing an automatic and intelligent water quality monitoring module, the efficiency and accuracy of water quality monitoring are remarkably improved, and by utilizing an advanced sensor and analysis technology, the system can monitor and analyze water quality data in real time and timely identify pollutants and microorganism activities, so that the improvement not only reduces the dependence on manual monitoring, but also ensures the real-time property and accuracy of the water quality data, thereby more effectively preventing and managing the water quality problem.
By means of integrated analysis of data from different modules, the system is capable of formulating a more effective water supply strategy based on real-time data, including intelligently selecting water sources, optimizing water treatment processes, and adjusting water supply networks to improve overall efficiency and response capacity of the water supply system.
According to the invention, the user feedback mechanism is realized, the feedback of the user on the water quality and the water supply service can be timely collected and responded, the intelligent mobile phone application and the online platform are designed to enable the user to easily report the problem and acquire the information, the participation and trust of the user are enhanced, and in addition, the system can better understand the user demand by analyzing the user feedback, so that the quality of the water supply service and the user satisfaction are improved.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a water supply operation and maintenance analysis system according to an embodiment of the present invention.
Description of the embodiments
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1, an unattended urban and rural water supply operation and maintenance analysis system comprises a water quality monitoring module, a water quality data analysis module, a water supply management module, a water quality risk assessment module, a user feedback module and a central data management unit; wherein,
and the water quality monitoring module is: the method comprises the steps of adopting a biosensor and a chemical analysis technology to monitor the microbial activity, the chemical substance content and the change thereof in water in real time so as to detect the existence of trace pollutants including heavy metals, organic pollutants and pathogens;
the water quality data analysis module: deep analysis is carried out on the data collected from the water quality monitoring module so as to predict the water quality change trend and identify potential pollution sources;
and a water supply management module: according to the water quality data analysis result, adjusting a water supply strategy, including water source selection, water treatment process optimization and water supply network adjustment;
a water quality risk assessment module; for assessing water quality related health and environmental risks and for issuing an early warning when a potential dangerous level is detected;
and a user feedback module: the intelligent mobile phone application and the online platform are used for collecting feedback of users on water quality and water supply service, and the module not only enhances user participation, but also provides precious data so as to further optimize water quality monitoring and water supply strategies;
a central data management unit: for coordinating the data operation and exchange of the various modules and communicating system-generated insights and suggestions to the operation and maintenance team and decision-makers.
The water quality monitoring module comprises a microorganism detection unit, a chemical analysis unit and a pollutant identification unit; wherein,
microorganism detection unit: a fluorescence-labeled biosensor is adopted for monitoring the activities of microorganisms in water in real time, the biosensor detects the microorganisms through biological markers (such as DNA sequences of specific microorganisms) and reflects the increase and decrease of the content of the microorganisms through the change of fluorescence signals;
chemical analysis unit: determining the content and the change of chemical substances in water by using high-performance liquid chromatography and mass spectrometry technology, wherein the liquid chromatography is used for separating various compounds in a water sample, and the mass spectrometry is used for identifying and quantifying the compounds;
contaminant identification unit: the infrared spectrum analysis and the electrochemical sensing technology are combined for detecting trace pollutants in real time, the infrared spectrum analysis can identify the spectrum characteristics of specific compounds, and the electrochemical sensor is used for detecting the concentration of heavy metals and organic pollutants in water.
The water quality data analysis module comprises a data preprocessing unit, a trend analysis unit, an abnormality detection unit and a source tracking unit; wherein,
a data preprocessing unit: preprocessing the collected water quality data by adopting a standardized and normalized processing method, wherein the preprocessing comprises removing noise, filling missing values and normalizing the data so as to ensure the quality and consistency of the data;
trend analysis unit: performing time series analysis by applying an autoregressive moving average model (ARMA), and analyzing historical and real-time data of water quality data to predict the trend and mode of water quality change, wherein the ARMA model formula is as follows:
wherein->Is the observation of the time point, +.>Is a constant term->Is a white noise item, < >>And->The order of the autoregressive and moving average parts of the model, respectively, +.>And->Parameters of the autoregressive and moving average models, respectively;
an abnormality detection unit: identifying abnormal points in the data set by an abnormal value detection method of the isolation forest, wherein the abnormal points represent predicted potential pollution events, and the implementation steps of the isolation forest algorithm are as follows:
a. randomly selecting a feature;
b. randomly selecting a segmentation value of the feature;
c. repeating the steps a-b to construct a plurality of isolation trees to form a forest;
the outliers are typically more easily isolated and therefore will be isolated earlier in the forest;
the anomaly score is based on path length, wherein the path length is the distance from the root node to the termination node, and the anomaly score is calculated by the following formula:
wherein->Is sample->Is (are) abnormality score>Is the sample size,/->Is sample->Average path length in all isolation trees, < >>Is a normalization factor for correcting the path length of different sized sample sets;
source tracking unit: the method comprises principal component analysis PCA and cluster analysis, and is used for identifying and tracking potential pollution sources.
The steps of identifying and tracking the potential pollution sources in the source tracking unit specifically comprise:
s1: collecting various water quality parameter data provided by a water quality monitoring module, including chemical substance content, heavy metal concentration and microorganism quantity;
s2: the collected data is subjected to dimension reduction processing by using a PCA method, and the PCA calculation formula is as follows:
wherein->Is the original data matrix, < >>Is a principal component weight matrix extracted from the data covariance matrix,/a>The principal component score matrix after conversion, which aims at reducing the dimension of data and simultaneously retaining the most important variation information;
s3: performing cluster analysis on the data subjected to PCA dimension reduction, and performing minimization of the sum of squares in the cluster by using a K-means clustering algorithm, wherein the specific formula is as follows:
wherein->Is the number of clusters, +.>Is->Data point set in each cluster, +.>Is->Center point of each cluster,/>Is a data point;
s4: based on the clustering results, different potential pollution sources will be identified, wherein each cluster represents a potential pollution pattern, and the geographic location, time plume and chemical composition characteristics of the pollution sources will be used to track and identify the pollution sources.
The water supply management module comprises a water source decision unit, a water treatment optimizing unit, a pipe network scheduling unit, a data feedback and adjustment unit and an emergency treatment unit; wherein,
the water source decision unit: analysis of contaminant content based on water quality dataAnd microbial Activity->Decision algorithm ∈>Selecting an optimal water source based on a preset threshold +.>And->Decision making is carried out, and a specific decision formula is as follows:
wherein, the water source->And->Respectively representing different water source options;
water treatment optimizing unit: according to the water quality monitoring resultAdjusting the water treatment process parameters->Specifically adjusting the dosage of the chemical treatment agent>The formula is: />Wherein->Is an adjustment coefficient for adjusting the addition amount of the chemical treatment agent according to the water quality condition;
a pipe network scheduling unit: using hydraulic modelsOptimizing the pipe network, wherein->Flow rate->Is a water quality parameter, the model is obtained by optimizing the valve +.>And pump station->The water supply is adjusted by setting, and the specific formula is as follows:
the summation is for all relevant valve and pump station operating costs;
the data feedback and adjustment unit: will actually supply waterFeedback to water quality data analysis module, using feedback adjustment algorithm +.>Continuously monitoring and adjusting a water supply strategy, the formula being expressed as:
wherein->Is a feedback adjustment coefficient for adjusting the water supply strategy according to the actual situation;
an emergency processing unit: when the serious water quality problem S is detected, starting an emergency treatment process, and switching to a standby water source when the water quality problem exceeds a critical value; otherwise, the water treatment measures are enhanced.
The water quality risk assessment module comprises a microorganism concentration analysis unit, a risk grade assessment unit and an early warning mechanism unit; wherein,
microorganism concentration analysis unit: the quantitative method is used for analyzing the concentration of specific chemical substances and microorganisms in a water sample, specifically, the spectral analysis technology is used for measuring the concentration of the specific chemical substances, and the concentration of the microorganisms is measured by a culture method or a molecular biological technology, and a specific calculation formula is shown as follows:
wherein->And->Conversion functions of chemical and microorganism concentration and detection response, respectively;
risk level evaluation unit: according to the concentration analysis result, the health and environmental risk level of the water quality is evaluated, and the safety threshold value of different pollutants is setAnd->The risk is classified into different grades according to the degree that the concentration exceeds a threshold value;
an early warning mechanism unit: when the water quality risk level reaches "high", an early warning mechanism is automatically triggered, including notifying the relevant management and emergency response team, and alerting the public if necessary.
The risk level is low, medium and high, and the specific calculation distinguishing standard is as follows:
proximity refers to a concentration range that approaches but does not exceed a safe interval value.
The user feedback module comprises a feedback collection unit, a data processing unit, a feedback analysis unit and a feedback response unit; wherein,
feedback collection unit: user feedback is collected through smart phone application and an online platform, and the user directly reports water quality problems, water supply interruption or other water supply service related problems through the platform;
a data processing unit: classifying and primarily analyzing the collected feedback data, and extracting key information by using a text analysis technology, wherein the key information comprises the type, time, place and influence degree of the problem;
feedback analysis unit: based on the processed data, performing a deep analysis to identify common problems or trends, using statistical analysis and data mining techniques, such as association rule mining and trend analysis, to find potential problems or user points of interest;
feedback response unit: and (3) formulating and implementing a response strategy according to the analysis result, wherein the strategy comprises informing related departments to carry out emergency repair, adjusting a water supply operation and maintenance plan or providing necessary information and guidance for users.
The central data management unit ensures real-time synchronization and consistency of data among different modules based on a data integration technology, wherein the data integration technology collects data of each module through a unified interface and protocol, and the data comprises water quality monitoring, user feedback and water management strategies; after the data is normalized and integrated, it is processed through data analysis tools, which may include machine learning algorithms and Complex Event Processing (CEP) techniques, for extracting insight and generating advice from the data, the analysis results are then communicated to the operation and maintenance team and decision maker through automated reporting systems and visualization dashboards to support data-based decisions, and the central data management unit continuously optimizes data processing methods and analysis parameters based on feedback from the operation and maintenance team and decision maker to ensure that the system is able to accommodate changing operation and maintenance requirements and environments.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (9)
1. The unmanned urban and rural water supply operation and maintenance analysis system is characterized by comprising a water quality monitoring module, a water quality data analysis module, a water supply management module, a water quality risk assessment module, a user feedback module and a central data management unit; wherein,
and the water quality monitoring module is: the method comprises the steps of adopting a biosensor and a chemical analysis technology to monitor the microbial activity, the chemical substance content and the change thereof in water in real time so as to detect the existence of trace pollutants;
the water quality data analysis module: deep analysis is carried out on the data collected from the water quality monitoring module so as to predict the water quality change trend and identify potential pollution sources;
and a water supply management module: according to the water quality data analysis result, adjusting a water supply strategy, including water source selection, water treatment process optimization and water supply network adjustment;
a water quality risk assessment module; for assessing water quality related health and environmental risks and for issuing an early warning when a potential dangerous level is detected;
and a user feedback module: collecting feedback of users on water quality and water supply service through smart phone application and an online platform;
a central data management unit: for coordinating the data operation and exchange of the various modules and communicating system-generated insights and suggestions to the operation and maintenance team and decision-makers.
2. The unmanned urban and rural water supply operation and maintenance analysis system according to claim 1, wherein the water quality monitoring module comprises a microorganism detection unit, a chemical analysis unit and a pollutant identification unit; wherein,
microorganism detection unit: a fluorescence-marked biosensor is adopted for monitoring the activities of microorganisms in water in real time, and the biosensor detects the microorganisms through the biological markers and reflects the increase and decrease of the microorganism content through the change of fluorescence signals;
chemical analysis unit: determining the content and variation of chemical substances in water by using high performance liquid chromatography and mass spectrometry techniques, wherein the liquid chromatography is used for separating various compounds in a water sample, and the mass spectrometry is used for identifying and quantifying the compounds;
contaminant identification unit: the method is used for detecting trace pollutants in real time by combining infrared spectrum analysis and electrochemical sensing technology, wherein the infrared spectrum analysis can identify the spectrum characteristics of specific compounds, and the electrochemical sensor is used for detecting the concentration of heavy metals and organic pollutants in water.
3. The system according to claim 2, wherein the water quality data analysis module comprises a data preprocessing unit, a trend analysis unit, an anomaly detection unit and a source tracking unit; wherein,
a data preprocessing unit: preprocessing the collected water quality data by adopting a standardized and normalized processing method, wherein the preprocessing comprises removing noise, filling missing values and normalizing the data so as to ensure the quality and consistency of the data;
trend analysis unit: performing time series analysis by applying an autoregressive moving average model (ARMA), and analyzing historical and real-time data of water quality data to predict the trend and mode of water quality change, wherein the ARMA model formula is as follows:
wherein->Is the observation of the time point, +.>Is a constant term->Is a white noise item, < >>And->The order of the autoregressive and moving average parts of the model, respectively, +.>And->Parameters of the autoregressive and moving average models, respectively；
An abnormality detection unit: identifying abnormal points in the data set by an abnormal value detection method of an isolation forest, wherein the abnormal points represent predicted potential pollution events, and the implementation steps of the isolation forest algorithm are as follows:
a. randomly selecting a feature;
b. randomly selecting a segmentation value of the feature;
c. repeating the steps a-b to construct a plurality of isolation trees to form a forest;
the anomaly score is based on path length, wherein the path length is the distance from the root node to the termination node, and the calculation formula of the anomaly score is as follows:
wherein->Is sample->Is (are) abnormality score>Is the sample size,/->Is sample->Average path length in all isolation trees, < >>Is a normalization factor for correcting the path length of different sized sample sets;
source tracking unit: the method comprises principal component analysis PCA and cluster analysis, and is used for identifying and tracking potential pollution sources.
4. An unmanned urban and rural water supply operation and maintenance analysis system according to claim 3, wherein the step of identifying and tracking the potential pollution source in the source tracking unit comprises:
s1: collecting various water quality parameter data provided by a water quality monitoring module, including chemical substance content, heavy metal concentration and microorganism quantity;
s2: performing dimension reduction processing on the collected data by using a PCA method, wherein the PCA calculation formula is as follows:
wherein->Is the original data matrix, < >>Is a principal component weight matrix extracted from the data covariance matrix,/a>Is the principal component score matrix after conversion;
s3: performing cluster analysis on the data subjected to PCA dimension reduction, and performing minimization of the sum of squares in the cluster by using a K-means clustering algorithm, wherein the specific formula is as follows:
wherein->Is the number of clusters, +.>Is->Data point set in each cluster, +.>Is->Center point of each cluster,/>Is a data point;
s4: based on the clustering results, different potential pollution sources will be identified, wherein each cluster represents a potential pollution pattern, and the geographic location, time plume and chemical composition characteristics of the pollution sources will be used to track and identify the pollution sources.
5. The system for analyzing the operation and maintenance of the unmanned urban and rural water supply according to claim 4, wherein the water supply management module comprises a water source decision unit, a water treatment optimizing unit, a pipe network scheduling unit, a data feedback and adjustment unit and an emergency treatment unit; wherein, the water source decision unit: analysis of contaminant content based on water quality dataAnd microbial Activity->Decision algorithm ∈>Selecting an optimal water source based on a preset threshold +.>And->Decision making is carried out, and a specific decision formula is as follows:
wherein, the water source->And->Respectively representing different water source options;
water treatment optimizing unit: according to the water quality monitoring resultAdjusting the water treatment process parameters->Specifically adjusting the dosage of the chemical treatment agent>The formula is:
wherein->Is an adjustment coefficient for adjusting the addition amount of the chemical treatment agent according to the water quality condition;
a pipe network scheduling unit: using hydraulic modelsOptimizing the pipe network, wherein->Flow rate->Is a water quality parameter, the model is obtained by optimizing the valve +.>And pump station->The water supply is adjusted by setting, and the specific formula is as follows:
；
the data feedback and adjustment unit: will actually supply waterFeedback to the water quality data analysis module, and using feedback adjustment algorithmContinuously monitoring and adjusting a water supply strategy, the formula being expressed as:
wherein->Is a feedback adjustment coefficient for adjusting the water supply strategy according to the actual situation;
an emergency processing unit: when the serious water quality problem S is detected, starting an emergency treatment process, and switching to a standby water source when the water quality problem exceeds a critical value; otherwise, the water treatment measures are enhanced.
6. The unmanned urban and rural water supply operation and maintenance analysis system according to claim 5, wherein the water quality risk assessment module comprises a microorganism concentration analysis unit, a risk level assessment unit and an early warning mechanism unit; wherein,
microorganism concentration analysis unit: the quantitative method is used for analyzing the concentration of specific chemical substances and microorganisms in a water sample, specifically, the spectral analysis technology is used for measuring the concentration of the specific chemical substances, and the concentration of the microorganisms is measured by a culture method or a molecular biological technology, and a specific calculation formula is shown as follows:
wherein->And->Conversion functions of chemical and microorganism concentration and detection response, respectively;
risk level evaluation unit: according to the concentration analysis result, the health and environmental risk level of the water quality is evaluated, and the safety threshold value of different pollutants is setAnd->The risk is classified into different grades according to the degree that the concentration exceeds a threshold value;
an early warning mechanism unit: when the water quality risk level reaches "high", an early warning mechanism is automatically triggered, including notifying the relevant management and emergency response team, and alerting the public if necessary.
7. The system for analyzing the operation and maintenance of the unmanned urban and rural water supply according to claim 6, wherein the risk level is three levels of low, medium and high, and the specific calculation distinguishing standard is as follows:
the proximity refers to a concentration range that approaches but does not exceed a safe interval value.
8. The system according to claim 7, wherein the user feedback module comprises a feedback collection unit, a data processing unit, a feedback analysis unit and a feedback response unit; wherein,
feedback collection unit: collecting user feedback through smart phone application and an online platform, wherein the user directly reports water quality problems, water supply interruption or other water supply service related problems through the platform;
a data processing unit: classifying and primarily analyzing the collected feedback data, and extracting key information by using a text analysis technology, wherein the key information comprises the type, time, place and influence degree of the problem;
feedback analysis unit: based on the processed data, performing a deep analysis to identify common problems or trends, using statistical analysis and data mining techniques to find potential problems or user concerns;
feedback response unit: and (3) formulating and implementing a response strategy according to the analysis result, wherein the strategy comprises informing related departments to carry out emergency repair, adjusting a water supply operation and maintenance plan or providing necessary information and guidance for users.
9. The system according to claim 8, wherein the central data management unit is based on a data integration technology, which is used for collecting data of each module through a unified interface and protocol, including water quality monitoring, user feedback and water supply management strategies, to ensure real-time synchronization and consistency of data among different modules.
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| CN118253059A (en) * | 2024-05-30 | 2024-06-28 | 济南瑞源智能城市开发有限公司 | Fire-fighting pool remote monitoring method based on Internet of things |
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| CN117951584A (en) * | 2024-03-13 | 2024-04-30 | 青岛启弘信息科技有限公司 | Ocean data processing and information scheduling system based on AI and Internet of things technology |
| CN118253059A (en) * | 2024-05-30 | 2024-06-28 | 济南瑞源智能城市开发有限公司 | Fire-fighting pool remote monitoring method based on Internet of things |
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