An online monitoring system and method for direct drinking water quality

Abstract

The present application relates to the field of water quality monitoring, and proposes a kind of direct drinking water quality online monitoring method, comprising: by identifying water supply source area and analyzing water-using crowd characteristics and surrounding environment, water sample is preliminarily classified, key component index is detected and potential risk is evaluated, monitoring project priority and monitoring frequency weight are determined, dynamic monitoring planning is generated, applicable detection standard system is constructed, combined with real-time flow parameter adjustment sampling amount, sampling optimization strategy is formulated, the best monitoring point is determined by pipeline topology processing, point arrangement plan is formulated, dynamic monitoring planning, sampling optimization strategy and monitoring point arrangement plan are integrated, monitoring task is executed using online monitoring cloud platform, and online monitoring scheme is generated.The present application can improve the online monitoring guarantee of direct drinking water quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of water quality monitoring, in particular to a direct drinking water quality online monitoring system and method. BACKGROUND

[0002] In contemporary society, with the significant improvement of people's living standards and the increasing awareness of health, the quality requirements for drinking water are becoming increasingly strict. Direct drinking water is increasingly widely used in families, offices and public areas due to its convenience and relatively pure characteristics. However, the water quality monitoring of direct drinking water faces many challenges.

[0003] Traditional water quality detection methods are mostly periodic sampling laboratory tests. This method has obvious drawbacks. On the one hand, the detection period is long, and it cannot reflect the dynamic changes of water quality in real time. If water pollution occurs during the interval between two detections, it is difficult to detect and handle in time. On the other hand, sampling detection has limitations. The sample is difficult to completely represent the overall water body situation, and it is easy to miss the local water quality deterioration area. It is difficult to meet the needs of large-scale direct drinking water supply system for continuous and accurate water quality monitoring, especially in some scenes with extremely high requirements for water quality stability, such as hospitals and schools. The traditional monitoring method cannot ensure timely detection of water quality abnormalities, which may pose a potential threat to the health of users. Therefore, a direct drinking water quality online monitoring system and method are needed to improve the online monitoring guarantee of direct drinking water quality. SUMMARY

[0004] The present application provides a direct drinking water quality online monitoring system and method, which mainly aims to improve the online monitoring guarantee of direct drinking water quality.

[0005] To achieve the above purpose, the present application provides a direct drinking water quality online monitoring system, which comprises a preliminary classification module, a monitoring planning module, a strategy construction module, a plan making module and a scheme generating module.

[0006] The preliminary classification module is used to obtain a direct drinking water sample, identify the water supply source area corresponding to the direct drinking water sample, analyze the water use population characteristics and surrounding environmental factors of the water supply source area, and preliminarily classify the direct drinking water sample based on the population characteristics and environmental factors to obtain water sample classification information.

[0007] The monitoring planning module is used to detect key component indicators in the water sample classification information, identify the potential risk level corresponding to the key component indicators, determine the monitoring project priority of the direct drinking water sample based on the potential risk level, calculate the monitoring frequency weight corresponding to the monitoring project priority, and generate a dynamic monitoring plan corresponding to the water supply area based on the monitoring frequency weight.

[0008] The strategy construction module is configured to construct a detection standard system applicable to the direct drinking water sample, analyze real-time flow parameters corresponding to the direct drinking water sample, calculate sampling amount adjustment values of the direct drinking water sample at different time periods based on the real-time flow parameters, and construct a sampling optimization strategy of the direct drinking water sample based on the detection standard system and the sampling amount adjustment values.

[0009] The plan development module is configured to perform topology processing on a layout of a delivery pipeline of the direct drinking water sample to obtain pipeline topology data, determine an optimal monitoring point corresponding to monitoring of the direct drinking water sample based on the pipeline topology data, query equipment installation parameters corresponding to the optimal monitoring point, and develop a point arrangement plan corresponding to a water supply source region based on the equipment installation parameters.

[0010] The scheme generation module is configured to determine a monitoring target task corresponding to the direct drinking water sample based on the dynamic monitoring plan, the sampling optimization strategy, and the monitoring point arrangement plan, execute the monitoring target task by using a preset direct drinking water online monitoring cloud platform to obtain task execution data, and generate an online monitoring scheme corresponding to the direct drinking water sample based on the task execution data.

[0011] Optionally, the direct drinking water sample is preliminarily classified based on the crowd characteristics and the environmental factors to obtain water sample classification information, including:

[0012] Crowd features and environmental features in the crowd characteristics and the environmental factors are extracted respectively.

[0013] Feature mapping rule sets corresponding to the direct drinking water sample are constructed based on the crowd features and the environmental features.

[0014] Water sample feature groups in the feature mapping rule sets are queried.

[0015] Water sample feature labels in the water sample feature groups are extracted.

[0016] The direct drinking water sample is preliminarily classified based on the water sample feature labels to obtain water sample classification information.

[0017] Optionally, the monitoring item priority corresponding to the direct drinking water sample is determined based on the potential risk level, including:

[0018] The potential risk level is divided into intervals to obtain risk division intervals.

[0019] Key component indicators of the direct drinking water sample are quantitatively processed to obtain indicator quantization values.

[0020] The indicator quantization values are matched to the risk division intervals to obtain risk indicator features.

[0021] cross-analyzing the risk indicator features to obtain a risk correlation matrix;

[0022] screening a matrix correlation item in the risk correlation matrix;

[0023] determining a monitoring item priority corresponding to the direct drinking water sample based on the matrix correlation item.

[0024] Optionally, the calculating of the monitoring frequency weight corresponding to the monitoring item priority comprises:

[0025] calculating the monitoring frequency weight corresponding to the monitoring item priority.

[0026] Optionally, the constructing of the detection standard system applicable to the direct drinking water sample comprises:

[0027] detecting a component composition corresponding to the direct drinking water sample;

[0028] extracting a core component index corresponding to the component composition;

[0029] inquiring an extraction technical specification corresponding to the core component index;

[0030] determining a standard extraction element corresponding to the direct drinking water sample based on the extraction technical specification;

[0031] constructing the detection standard system applicable to the direct drinking water sample based on the standard extraction element.

[0032] Optionally, the calculating of the sampling amount adjustment value of the direct drinking water sample in different time periods based on the real-time flow parameter comprises:

[0033] calculating the sampling amount adjustment value of the direct drinking water sample in different time periods.

[0034] Optionally, the topological processing of the conveying pipeline layout of the direct drinking water sample to obtain pipeline topological data comprises:

[0035] obtaining a conveying water pipeline corresponding to the direct drinking water sample;

[0036] inquiring a conveying pipeline layout corresponding to the conveying water pipeline;

[0037] collecting basic layout information corresponding to the conveying pipeline layout;

[0038] identifying a key layout node in the basic layout information;

[0039] topologically processing the key layout node to obtain pipeline topological data.

[0040] Optionally, the best monitoring point corresponding to the direct drinking water sample is determined based on the pipeline topology data, comprising:

[0041] Extracting key topological features in the pipeline topology data;

[0042] Based on the key topological features, the potential monitoring area corresponding to the direct drinking water sample is screened;

[0043] Analyze the area sensitive unit corresponding to the potential monitoring area;

[0044] Calculate the water quality sensitive index corresponding to the area sensitive unit;

[0045] Based on the water quality sensitive index, the best monitoring point corresponding to the direct drinking water sample is determined.

[0046] Optionally, the point layout plan corresponding to the water supply source area is formulated based on the equipment installation parameters, comprising:

[0047] Feature extraction is performed on the equipment installation parameters to obtain parameter feature information;

[0048] Query the point layout set matched with the parameter feature information;

[0049] Integrate the location parameters of the water supply source area with the point layout set to obtain integrated layout data;

[0050] Analyze the point optimization direction corresponding to the integrated layout data;

[0051] Based on the point optimization direction, the point layout plan corresponding to the water supply source area is formulated.

[0052] Optionally, to solve the above problems, the present application provides a direct drinking water quality online monitoring method, comprising:

[0053] Obtain a direct drinking water sample, identify the water supply source area corresponding to the direct drinking water sample, analyze the water consumption population characteristics and surrounding environmental factors of the water supply source area, and based on the population characteristics and the environmental factors, the direct drinking water sample is classified to obtain water sample classification information;

[0054] Detect the key component index in the water sample classification information, identify the potential risk level corresponding to the key component index, determine the monitoring project priority corresponding to the direct drinking water sample based on the potential risk level, calculate the monitoring frequency weight corresponding to the monitoring project priority, and generate the dynamic monitoring plan corresponding to the water supply area based on the monitoring frequency weight;

[0055] constructing a detection standard system applicable to the direct drinking water sample, analyzing a real-time flow parameter corresponding to the direct drinking water sample, calculating a sampling amount adjustment value of the direct drinking water sample in different time periods based on the real-time flow parameter, and constructing a sampling optimization strategy of the direct drinking water sample based on the detection standard system and the sampling amount adjustment value;

[0056] topologically processing a layout of a conveying pipeline of the direct drinking water sample to obtain pipeline topological data, determining an optimal monitoring point corresponding to monitoring of the direct drinking water sample based on the pipeline topological data, querying equipment installation parameters corresponding to the optimal monitoring point, and formulating a point layout plan corresponding to a water supply source region based on the equipment installation parameters;

[0057] determining a monitoring target task corresponding to the direct drinking water sample based on the dynamic monitoring plan, the sampling optimization strategy, and the monitoring point layout plan, executing the monitoring target task by using a preset direct drinking water online monitoring cloud platform to obtain task execution data, and generating an online monitoring scheme corresponding to the direct drinking water sample based on the task execution data.

[0058] Firstly, the application can improve the monitoring accuracy by acquiring direct drinking water samples, identifying the water supply source area corresponding to the direct drinking water samples, and implementing monitoring strategies according to the unique characteristics of different water supply areas, such as the water quality basis of the water source and the surrounding environmental conditions, which helps to quickly locate the specific area with water quality problems and take timely measures to effectively ensure water safety. The application can help accurately locate the weak links of direct drinking water quality by detecting key component indicators in the water sample classification information and identifying the potential risk level corresponding to the key component indicators, which can provide a basis for timely taking targeted improvement measures and make resources focus on monitoring and processing of high-risk component indicators, effectively ensuring the stability and safety of direct drinking water supply and reducing health risks caused by water quality problems. The application can provide a clear and unified reference for the quality evaluation of direct drinking water by constructing a detection standard system suitable for the direct drinking water samples, ensuring that the water quality meets the safety and health requirements, protecting the public's drinking water safety, helping to standardize the production and detection processes of water supply enterprises, improving the overall level of the industry, and promoting the healthy development of the direct drinking water industry. The application can help accurately grasp the connection mode and direction of the pipeline by performing topology processing on the layout of the direct drinking water sample conveying pipeline to obtain pipeline topology data, which can quickly locate the problem area during pipeline maintenance and fault troubleshooting, reduce water stop time and maintenance cost, optimize pipeline design, reasonably plan new pipeline laying according to topology data, improve water supply efficiency, and provide a basis for scientific arrangement of water quality monitoring points to ensure the quality and safety of direct drinking water during transportation. Further, the application determines the monitoring target task corresponding to the direct drinking water samples based on the dynamic monitoring plan, sampling optimization strategy and monitoring point arrangement plan, which can make the monitoring work more systematic and targeted, accurately focus on key links according to various plans, improve monitoring efficiency, ensure comprehensive and accurate monitoring data, and help optimize resource allocation. Therefore, the direct drinking water quality online monitoring system and method provided by the application can improve the online monitoring of direct drinking water quality. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 A functional module diagram of a direct drinking water quality online monitoring system provided by an embodiment of the application is shown in the figure.

[0060] Figure 2 A flowchart of the direct drinking water quality online monitoring method provided by an embodiment of the application is shown in the figure.

[0061] The purpose of the application, functional characteristics and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0062] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0063] In addition, the step sequence in each method embodiment described below is only an example and is not strictly limited.

[0064] In fact, the server device deployed by a straight drinking water quality online monitoring system can be composed of one or more devices. The straight drinking water quality online monitoring system can be implemented as a business instance, a virtual machine, or a hardware device. For example, the straight drinking water quality online monitoring system can be implemented as a business instance deployed on one or more devices in a cloud node. In short, the straight drinking water quality online monitoring system can be understood as a software deployed on a cloud node, which provides a straight drinking water quality online monitoring service for each user end. Alternatively, the straight drinking water quality online monitoring system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. The virtual machine has application software installed therein for managing each user end. Alternatively, the straight drinking water quality online monitoring system can also be implemented as a server composed of a plurality of same or different types of hardware devices, and one or more hardware devices are provided for providing a straight drinking water quality online monitoring service for each user end.

[0065] In terms of implementation, a straight drinking water quality online monitoring system and a user end are mutually adapted. That is, the straight drinking water quality online monitoring system is an application installed on a cloud service platform, and the user end is a client establishing a communication connection with the application; or the straight drinking water quality online monitoring system is implemented as a website, and the user end is implemented as a webpage; or the straight drinking water quality online monitoring system is implemented as a cloud service platform, and the user end is implemented as an applet in an instant messaging application.

[0066] Referring to Figure 1 FIG. 1 is a functional module diagram of a straight drinking water quality online monitoring system according to an embodiment of the present application.

[0067] The online direct drinking water quality monitoring system 100 can be set in the cloud server, and in the implementation form, can serve as one or more service devices, can be installed as an application on the cloud (such as a direct drinking water quality online monitoring server, a server cluster, etc.), or can be developed as a website. According to the function of the implementation, the online direct drinking water quality monitoring system 100 comprises a preliminary classification module 101, a monitoring planning module 102, a strategy construction module 103, a plan making module 104 and a scheme generation module 105.

[0068] In the embodiment of the application, in the tracking of the online direct drinking water quality monitoring, each of the above modules can be independently implemented and called by other modules. The calling here can be understood as that a module can be connected to multiple modules of another type, and provide corresponding services for the connected multiple modules. In the online direct drinking water quality monitoring system provided by the embodiment of the application, the application range of the online direct drinking water quality monitoring architecture can be adjusted by increasing modules and directly calling without modifying program codes, cluster horizontal expansion is realized, so as to achieve the purpose of quickly and flexibly expanding the online direct drinking water quality monitoring system. In actual application, the above modules can be set in the same device or different devices, or can be set in a virtual device, such as a service instance in a cloud server.

[0069] The following will be described in combination with specific embodiments, respectively for each component of the online direct drinking water quality monitoring system and the specific work flow.

[0070] The preliminary classification module 101 is used for obtaining a direct drinking water sample, identifying a water supply source area corresponding to the direct drinking water sample, analyzing the water use population characteristics and the surrounding environmental factors of the water supply source area, performing preliminary classification on the direct drinking water sample based on the population characteristics and the environmental factors, and obtaining water sample classification information.

[0071] By obtaining a direct drinking water sample and identifying a water supply source area corresponding to the direct drinking water sample, the application can implement monitoring strategies according to the unique characteristics of different water supply areas, such as water quality basis of water source, surrounding environmental conditions, etc., improve monitoring accuracy, and help subsequent rapid positioning of specific areas with water quality problems, so that measures can be taken in time when the problem occurs, and water safety can be effectively ensured.

[0072] The direct drinking water sample refers to a certain amount of direct drinking water sample collected from a direct drinking water supply system for water quality detection and analysis, which can represent the actual situation of direct drinking water at a specific time period and location, covering various material components, microbial content and other key elements reflecting water quality conditions contained in direct drinking water; the water supply source area refers to the initial water intake location of direct drinking water or the specific geographical range covered by the water supply network, which includes the water source (such as groundwater well, surface water intake point, etc.) and the area involved in the entire water supply path from the water source to the user end, each water supply source area has its unique geographical, environmental, infrastructure and other characteristics, which will affect the water quality of direct drinking water, for example, the surrounding industrial activities, agricultural pollution, population density, and the material and aging degree of the water supply pipeline, etc. factors, optionally, the identification of the water supply source area corresponding to the direct drinking water sample can be realized by a region identification tool, such as: GIS tool, according to the connection relationship between the collection point and the surrounding water supply pipeline, the water source or the area where the water supply plant is located is traced along the pipeline network to determine the water supply source area.

[0073] Further, by analyzing the water-using population characteristics and surrounding environmental factors of the water supply source area, the special needs of different populations for water quality and the possible impact on water quality can be predicted, so as to more accurately set the water quality monitoring indicators and frequency, protect the health of the population, and can identify potential pollution sources in advance, assess the pollution risk of the water supply water quality, help to take targeted prevention and control measures, and improve the stability and safety of direct drinking water quality.

[0074] The water-using population characteristics refer to the comprehensive characteristics of the age structure, health status, occupation type, living habits and water-using behavior mode of the water-using population, for example, the elderly population may be more sensitive to the mineral content in water and microbial safety; people engaged in certain occupations (such as chemical industry) may pay more attention to certain harmful substances in water due to their working environment; in terms of living habits, high water consumption families or units; the surrounding environmental factors refer to the geographical and geomorphic characteristics, land use type, industrial distribution, agricultural production activity intensity, resident aggregation degree and ecological environment condition of the water source and surrounding area of the water supply area, such as the existence of industrial enterprises in the surrounding area, which may cause heavy metal or chemical pollution risk; frequent agricultural activities in the area may cause non-point source pollution of water source due to the use of pesticides and fertilizers; the rock type in the geographical and geomorphic characteristics may affect the mineral content in water; the ecological environment is also related to the self-purification capacity of the water source, and these factors together constitute the external environmental conditions affecting the water quality of direct drinking water, optionally, the analysis of the water-using population characteristics and surrounding environmental factors of the water supply source area can be realized by a method, tool or algorithm, such as: (and examples of methods, tools or algorithms that can be realized are given to obtain water-using population characteristics and surrounding environmental factors).

[0075] Further, the application classifies the direct drinking water samples based on the population characteristics and the environmental factors to obtain water sample classification information, which can make the water sample classification more targeted according to the differences in water demand and habits of different populations and the pollution risk status of the surrounding environment, for example, the key indicators of water samples in high demand population or high pollution risk area can be monitored, and the resource allocation is optimized.

[0076] Among them, the water sample classification information refers to the detailed and systematic result summary obtained after the preliminary classification of all direct drinking water samples, including the category to which each water sample belongs (identified by a water sample feature label), the detailed description of the population characteristics and environmental characteristics corresponding to the water sample of the category, the common problems and key monitoring indicators of the water quality in the background of the population and the environment, the quantity distribution of the water samples of each category and the geographical distribution position in the water supply network and other information.

[0077] As an embodiment of the application, the water sample classification information obtained by classifying the direct drinking water samples based on the population characteristics and the environmental factors includes: extracting the population characteristics and the environmental characteristics in the population characteristics and the environmental factors respectively; constructing a feature mapping rule set corresponding to the direct drinking water samples based on the population characteristics and the environmental characteristics; querying a water sample feature group in the feature mapping rule set; extracting a water sample feature label in the water sample feature group; and classifying the direct drinking water samples based on the water sample feature label to obtain water sample classification information.

[0078] The population characteristics refer to, for example, the type of occupation (including different types of occupations such as industrial workers, agricultural workers, office workers, service industry personnel, and the characteristics of their working environment), income level (reflected in the differences in water-using equipment and water-using habits, high-income groups may be more inclined to use high-end water purification equipment, and the amount of water used and the requirements for water quality may also be different), education level (related to the awareness of water quality safety knowledge and the degree of refinement of water quality requirements), and the like. The environmental characteristics refer to the natural and man-made environmental characteristics of the water source and the surrounding area of the water supply area. The natural environment includes geographical and geomorphological characteristics (such as mountainous areas, plains, river and lake distribution, etc., different landforms may affect the type and quality of the water source, and mountainous areas may have more minerals dissolved in the water, while the water source in the plain area may be affected by the soil type), climate conditions (precipitation pattern, air temperature variation, humidity, etc., the water source replenishment and water quality variation in areas with more precipitation are different from those in arid areas, and air temperature and humidity affect the growth and reproduction of microorganisms in water), man-made environmental characteristics include urban construction and population settlement (urban sewage treatment facility construction, garbage disposal method, population density and distribution, domestic sewage discharge and landfill leachate in densely populated areas may affect the water quality of the water source, and the construction of a perfect urban infrastructure helps to protect the water source), traffic conditions (distance from major traffic routes to the water source, traffic flow, traffic pollution may contaminate the water source through atmospheric deposition and surface runoff), and the like. The characteristic mapping rule set refers to a systematic rule set that establishes a connection between the population characteristics and environmental characteristics and the classification attributes of the direct drinking water sample. For example, for the infant population in the population characteristics (which has specific requirements for the mineral content in water and low tolerance to harmful substances) and the surrounding area of the high-quality water source in the environmental characteristics (which has a good ecological environment and low pollution risk), a rule is established to map them to a type of water sample. This type of water sample will focus on monitoring the microbial indicators and the appropriate mineral content range in subsequent monitoring. The water sample characteristic group refers to a plurality of different sets formed by grouping direct drinking water samples with similar population characteristics and environmental characteristics combinations according to the characteristic mapping rule set. For example, a group of water samples may come from an aging community and the surrounding area with light agricultural activities. This group of water samples has common characteristics, such as containing a certain amount of agricultural non-point source pollution-related substances in the water, and considering the health needs of the elderly, the water quality hardness and microbial indicators have specific requirements. The water sample characteristic label refers to a concise and clear identification of each water sample characteristic group, which is used to quickly distinguish and identify different types of water sample characteristic groups. For example, for the water sample characteristic group of the aging community with light agricultural activities in the surrounding area, the water sample characteristic label can be "aging community-light agricultural pollution-hardness and microbial monitoring type". For the water sample characteristic group of the emerging industrial park with young office workers as the main residential population, the label can be "young park-potential industrial pollution-taste and convenience focus type", and the like.

[0079] Further, the population characteristics and environmental characteristics in the population characteristics and the environmental factors can be extracted by feature extraction tools, such as WireShark, Tcptrace and the like; the feature mapping rule set corresponding to the direct drinking water sample can be constructed by a machine learning algorithm, such as using a decision tree algorithm to construct a decision tree model according to known population characteristics, environmental characteristics and corresponding water quality classification results as training data; the water sample feature group in the feature mapping rule set can be queried by a rule engine tool, such as using Drools and the like rule engine, taking the population and environmental characteristics of the water sample as fact input, and the rule engine matches according to the pre-defined feature mapping rule set to return the corresponding water sample feature group; the water sample feature label in the water sample feature group can be extracted by a label generation template, such as filling the key information in the water sample feature group into the template to generate a label according to a pre-defined label generation template; and the direct drinking water sample can be preliminarily classified by a classification model, such as using a support vector machine and the like model, taking the extracted water sample feature label or feature group as input features, using known classified water sample data for training, and then inputting the water sample to be classified into the model for classification.

[0080] The monitoring planning module 102 is configured to detect a key component indicator in the water sample classification information, identify a potential risk level corresponding to the key component indicator, determine a monitoring item priority corresponding to the direct drinking water sample based on the potential risk level, calculate a monitoring frequency weight corresponding to the monitoring item priority, and generate a dynamic monitoring plan corresponding to the water supply area based on the monitoring frequency weight.

[0081] By detecting the key component indicator in the water sample classification information and identifying the potential risk level corresponding to the key component indicator, the application can help to accurately locate the weak link of the direct drinking water quality, can early warn the possible water quality deterioration according to the risk level, can provide a basis for timely taking targeted improvement measures, can make resources be allocated to the monitoring and processing of high-risk component indicators, and can effectively guarantee the stability and safety of the direct drinking water supply and reduce health risks caused by water quality problems.

[0082] The key component index refers to a core parameter directly related to the safety and health of direct drinking water quality, including microbial indicators, such as total bacterial count, coliform group, and the presence and content of pathogenic bacteria (such as Vibrio cholerae and Salmonella); chemical substance indicators, such as heavy metal (mercury, lead, cadmium, chromium, arsenic) content; the potential risk level refers to a level divided according to the content level, toxicity degree, and potential harm degree to human health and the environment of the key component index, which can be generally divided into a low risk level, indicating that the index value is within the safety standard range or close to the safety upper limit, and the water quality basically meets the requirements but needs continuous monitoring; a medium risk level, meaning that the index exceeds the safety standard to a certain extent, which will have a slight to moderate adverse effect on human health, such as a slight increase in the incidence of certain chronic diseases caused by long-term drinking, at which time appropriate improvement or treatment measures need to be taken and the monitoring frequency needs to be increased; a high risk level, indicating that the key component index is seriously over-standard, which will pose a serious threat to human health, such as acute poisoning and acute organ damage, and optionally, the key component index in the detection of the water sample classification information can be realized by instrument analysis tools, such as AAS, GC-MS, IC, etc.; the potential risk level corresponding to the key component index can be realized by a level identification method, such as the analytic hierarchy process and the risk matrix method.

[0083] Further, based on the potential risk level, the monitoring item priority of the direct drinking water sample is determined, which can reasonably allocate limited monitoring resources, concentrate more manpower, material resources and financial resources on the key monitoring items of high-risk-level water samples, ensure timely and accurate discovery of potential water quality problems, effectively prevent health risks caused by deteriorating water quality, and improve the efficiency and reliability of direct drinking water quality protection.

[0084] The monitoring item priority refers to the order arrangement of each monitoring item (corresponding to the detection item of different key component indicators) of the direct drinking water sample according to the matrix correlation item screened from the risk correlation matrix.

[0085] As an embodiment of the present application, the determination of the monitoring item priority of the direct drinking water sample based on the potential risk level comprises: interval division of the potential risk level to obtain a risk division interval; quantitative processing of the key component indicators of the direct drinking water sample to obtain an index quantitative value; matching the index quantitative value to the risk division interval to obtain a risk index feature; cross analysis of the risk index feature to obtain a risk correlation matrix; screening the matrix correlation item in the risk correlation matrix; and determining the monitoring item priority of the direct drinking water sample based on the matrix correlation item.

[0086] The risk division interval refers to dividing potential risk levels according to relevant standards of direct drinking water quality, past experience, risk assessment requirements, etc. into different interval sections according to certain numerical ranges or qualitative descriptions, for example, low-risk interval (all key component indicators of water quality are stable within the safety standard, and have little effect on human health), medium-risk interval (some indicators are close to or slightly exceed the safety standard, and there is a possibility of health impact), high-risk interval (key component indicators significantly exceed the standard, and there is a greater threat to human health), etc. The index quantitative value refers to the specific numerical value obtained by detecting various key component indicators (such as heavy metal content, microbial quantity, chemical substance concentration, etc.) in the direct drinking water sample through professional water quality detection means, and after standardization, normalization, etc. Quantitative processing, which can directly reflect the actual content of each component indicator in the water sample. The risk index feature refers to the comprehensive feature description formed by the specific indicators and the corresponding risk interval when the key component indicator quantitative value of the direct drinking water sample is matched to the corresponding risk division interval. The risk association matrix refers to a two-dimensional matrix constructed with different key component indicators as row elements and each risk division interval as column elements. By cross-analyzing different risk index features, the association of each index in different risk intervals, the mutual influence degree, etc. are filled in, and the complex relationship network between each index and the risk interval is directly displayed, providing a comprehensive data presentation form for mining key associations and identifying key monitoring directions. The matrix association item refers to the key representative elements selected according to certain importance judgment standards (such as the weight of the index on the risk, the degree of close association, etc.) in the risk association matrix, that is, those specific items that reflect the close and important relationship between key component indicators and risk levels.

[0087] Further, the interval division of the potential risk level can be realized by interval division methods, such as equidistant division method, cluster analysis method, etc.; the quantitative processing of the key component indicators of the direct drinking water sample can be realized by data standardization algorithms, such as Z-Score standardization algorithm; the cross analysis of the risk indicator characteristics can be realized by association rule mining algorithms, such as finding out the mutual relationship of different indicators under different risk levels by analyzing the frequent item sets and association rules between the key component indicators and the risk levels in a large amount of water sample data; the screening of the matrix association items in the risk association matrix can be realized by feature selection algorithms, such as calculating the information entropy of each element in the risk association matrix, the lower the information entropy, the more certain and important the information contained in the element, thereby screening the elements with information entropy lower than a set threshold as the matrix association items; the determination of the monitoring item priority corresponding to the direct drinking water sample can be realized by multi-attribute decision methods, such as determining multiple evaluation attributes according to the key indicators screened from the risk association matrix, and quantitatively evaluating the performance of each monitoring item under these attributes.

[0088] Further, by calculating the monitoring frequency weight corresponding to the monitoring item priority, the application can reasonably allocate limited monitoring resources according to the criticality of each monitoring item, ensure that high-priority items are monitored more frequently, timely and accurately capture water quality change trends, effectively prevent potential water quality deterioration risks, and thus provide strong protection for the quality and safety of direct drinking water.

[0089] The monitoring frequency weight refers to a value calculated by a specific formula, which is used to measure the importance of the monitoring frequency corresponding to the monitoring item priority, and comprehensively considers the risk factors and their correlation, as well as the quantitative values of the monitoring item attribute characteristics, to determine the relative frequency of each monitoring item that should be monitored in terms of resource allocation and time arrangement.

[0090] As an embodiment of the application, the calculation of the monitoring frequency weight corresponding to the monitoring item priority comprises:

[0091] The monitoring frequency weight corresponding to the monitoring item priority is calculated by the following formula:

[0092]

[0093] Wherein, CP represents the monitoring frequency weight corresponding to the monitoring item priority, p represents the total number of risk factors corresponding to the monitoring item, k represents the index of the number of risk factors, A k represents the risk degree value corresponding to the kth risk factor, B krepresents the correlation weight of the kth risk factor corresponding to the monitoring project priority, q represents the total number of attribute characteristics corresponding to the monitoring project, 1 represents the index of the number of product attribute characteristics, C l represents the characteristic quantization value corresponding to the lth attribute characteristic.

[0094] In detail, the monitoring project refers to a specific task or activity of observing, measuring and evaluating a specific object or process. In the water quality monitoring scenario, the monitoring project can include detection of various chemical substances (such as heavy metals, organic matter), physical parameters (such as temperature, turbidity), biological indicators (such as the number of microorganisms), etc. in water; the risk factor refers to various factors that can adversely affect the monitoring project or cause the monitoring target to fail to achieve, in water quality monitoring, the risk factors can include water source pollution, treatment process failure, pipeline corrosion, external environmental changes (such as floods, earthquakes and other natural disasters), etc.; the risk degree value refers to the quantitative evaluation of the risk brought by each risk factor, for example, for the risk factor of water source pollution, the risk degree value can be comprehensively evaluated according to the type, concentration and potential harm degree of the pollutant; the correlation weight refers to the quantitative value of the correlation degree between each risk factor and the monitoring project priority, for example, if a certain risk factor (such as a sudden increase of a certain pollutant in the water source) is closely related to the monitoring project (such as monitoring of the pollutant); the attribute characteristic refers to various characteristics and parameters possessed by the monitoring project itself, in water quality monitoring, the attribute characteristics can include the complexity of the monitoring method, the precision of the required equipment, the stability of the detection result, the length of the monitoring period, etc.; the characteristic quantization value refers to the quantitative representation of the attribute characteristics of the monitoring project, for example, for the complexity of the monitoring method, the number of operation steps, the level of required professional skills, etc. can be quantitatively scored to obtain the corresponding characteristic quantization value.

[0095] Further, based on the monitoring frequency weight, the application generates a dynamic monitoring plan corresponding to the water supply area, which can reasonably allocate monitoring resources, ensure that high-weight projects are monitored more frequently, ensure water quality safety, discover water quality change trends in time, and warn potential risks in advance, which helps to optimize the monitoring process and improve the overall water quality monitoring efficiency and management level of the water supply area.

[0096] Among them, the dynamic monitoring plan refers to the reasonable arrangement of time and resources for monitoring projects in the water supply area according to the monitoring frequency weight, which can be changed according to the risk factors of each monitoring project, the correlation weight and the attribute characteristics of the project itself, optionally, the generation of the dynamic monitoring plan corresponding to the water supply area can be realized by a planning generation tool, such as Tre l l o, Asana and other tools.

[0097] The strategy construction module 103 is configured to construct a detection standard system applicable to the direct drinking water sample, analyze a real-time flow parameter corresponding to the direct drinking water sample, calculate a sampling amount adjustment value of the direct drinking water sample at different time periods based on the real-time flow parameter, and construct a sampling optimization strategy of the direct drinking water sample based on the detection standard system and the sampling amount adjustment value.

[0098] The present application can provide a clear and unified reference for quality evaluation of direct drinking water by constructing the detection standard system applicable to the direct drinking water sample, ensure that the water quality meets the safety and health requirements, protect the public drinking water safety, help to standardize the production and detection process of water supply enterprises, improve the overall level of the industry, and promote the healthy development of the direct drinking water industry.

[0099] The detection standard system refers to a set of rules and regulations constructed based on standard extraction elements according to scientific and reasonable logical architecture and hierarchical relationship, which is used for comprehensive and standardized quality detection and evaluation of the direct drinking water sample, and covers multiple aspects such as basic standards (term definition, symbol specification, etc.), method standards (various detection method detailed processes), quality standards (limit value and grading of each core component index), and management standards (detection laboratory operation, personnel qualification, etc.).

[0100] As an embodiment of the present application, the construction of the detection standard system applicable to the direct drinking water sample comprises: detecting the component composition corresponding to the direct drinking water sample; extracting the core component index corresponding to the component composition; querying the extraction technical specification corresponding to the core component index; determining the standard extraction element corresponding to the direct drinking water sample based on the extraction technical specification; and constructing the detection standard system applicable to the direct drinking water sample based on the standard extraction element.

[0101] The component composition refers to the specific composition obtained after comprehensive analysis of various types of substances contained in the direct drinking water sample, covering bacteria, viruses, fungi and other microorganisms, heavy metals (such as lead, mercury, cadmium, etc.), inorganic ions (such as chloride ions, sulfate ions, etc.), organic compounds (such as pesticide residues, polycyclic aromatic hydrocarbons, etc.); the core composition index refers to the key indicator parameters selected and refined from the component composition of the direct drinking water sample, based on factors such as the degree of influence on human health, water quality safety relevance, and importance in reflecting the overall water quality, such as the types and content of pathogenic microorganisms in the microorganism index, the heavy metal exceeding multiple, the concentration of toxic and harmful organic matter in the chemical substance index, etc.; the extraction technology specification refers to the scientific and standardized operation requirements and criteria set for the core composition index to obtain its numerical value, which details the correct way of sample collection (such as sampling location, sampling amount, sampling container requirements, etc.), the use of professional detection methods (such as atomic absorption spectrometry for heavy metal detection, gas chromatography for organic pollutant detection, etc.); the standard extraction element refers to the key elements necessary for building a detection standard system extracted from the extraction technology specification, including the specific limit value range of the core composition index (such as the safe upper limit value of the content of a certain heavy metal), the key operation parameters in the detection method (such as the key wavelength setting and reaction time in instrument detection), the key control points in the quality control link (such as the allowable range of deviation in parallel sample detection), and the key nodes of the corresponding process steps.

[0102] Further, the detection of the component composition corresponding to the direct drinking water sample can be realized by spectral analysis tools, such as visible spectrophotometer, infrared spectrometer, etc.; the extraction of the core composition index corresponding to the component composition can be realized by index extraction methods, such as PCA, AHP, etc.; the query of the extraction technology specification corresponding to the core composition index can be realized by database retrieval system, such as by inputting the name of the core composition index (such as "water lead content detection technology specification") to retrieve relevant academic literature, national standards, industry standards, etc. in these databases to obtain detailed extraction technology specification; the determination of the standard extraction element corresponding to the direct drinking water sample can be realized by data analysis algorithm, such as keyword extraction algorithm, clustering algorithm, etc.; the construction of the detection standard system applicable to the direct drinking water sample can be realized by system engineering method, such as using Hall's three-dimensional structure model to construct the detection standard system from three dimensions of time, logic and knowledge (knowledge fields of water quality detection, standard formulation regulations, management science, etc.).

[0103] The application can accurately master the dynamic change of water supply by analyzing the real-time flow parameter corresponding to the direct drinking water sample, optimize the operation scheduling of the water supply system, detect whether there is leakage and other abnormal conditions in the pipe network in time, reduce the unnecessary loss of water resources, help to evaluate the working efficiency of the water treatment equipment, provide key basis for the maintenance and upgrading of the equipment, and ensure the efficiency and safety of direct drinking water supply.

[0104] The real-time flow parameter refers to the value related information of direct drinking water flowing in each link such as a water supply pipeline, a water treatment equipment and a water terminal at a specific time point or time period, including the instantaneous flow rate of water flow, that is, the water amount through a certain section per unit time, which can reflect the speed of the current water flow, and the cumulative flow, that is, the total amount of direct drinking water passing through a certain position within a certain time, which can intuitively present the total water supply or water consumption in a certain stage.

[0105] Further, the application calculates the sampling amount adjustment value of the direct drinking water sample in different time periods based on the real-time flow parameter, can accurately determine the sampling amount according to the flow change, ensure that the detection sample is representative, make the detection result more in line with the actual water quality condition, avoid too much or too little sampling caused by flow fluctuation, optimize the allocation of sampling resources, improve the sampling efficiency and reduce the cost.

[0106] The sampling amount adjustment value refers to the value for adjusting the sampling amount of the direct drinking water sample in different time periods, which comprehensively considers the influences of flow fluctuation, water quality change and external environmental factors and the like.

[0107] As an embodiment of the application, the calculation of the sampling amount adjustment value of the direct drinking water sample in different time periods based on the real-time flow parameter comprises:

[0108] The sampling amount adjustment value of the direct drinking water sample in different time periods is calculated by the following formula:

[0109]

[0110] Wherein, CZ represents the sampling amount adjustment value of the direct drinking water sample in different time periods, n represents the number of flow fluctuation stages, i represents the number index of the flow fluctuation stage, Q i represents the average flow in the i-th flow fluctuation stage, R i represents the water quality change weight corresponding to the i-th flow fluctuation stage, V i represents the sampling effectiveness coefficient of the i-th flow fluctuation stage, m represents the total number of external environmental factors, j represents the number index of the external environmental factor, and F jD represents the influence coefficient corresponding to the jth external environmental factor j D represents the environmental adjustment value of the jth external environmental factor j D represents the normalization constant.

[0111] In detail, the flow fluctuation stage refers to a period of time in which the real-time flow of direct drinking water changes significantly within a certain time range, for example, during the peak water consumption period, the flow may rapidly rise and remain at a high level, which is a flow fluctuation stage; during the night low water consumption period, the flow remains at a low level, which is also a flow fluctuation stage; the average flow refers to the average value of the direct drinking water flow within the i th flow fluctuation stage, for example, within a 1-hour peak water consumption flow fluctuation stage, the flow is recorded every 1 minute, and the average flow of the stage is obtained by adding the 60 flow data and dividing by 60; the water quality change weight refers to the quantitative weight of the water quality change degree corresponding to the i th flow fluctuation stage, which reflects the influence degree of the water quality change caused by the flow change on the sampling amount adjustment within the flow fluctuation stage; the sampling effectiveness coefficient refers to; the external environmental factor refers to the factor that affects the direct drinking water quality and flow, but does not belong to the direct drinking water supply system itself, these factors can include weather conditions (such as rainfall, air temperature), seasonal changes, surrounding environmental pollution, etc.; the environmental adjustment value refers to the quantitative value of the jth external environmental factor corresponding to the sampling amount adjustment, which reflects the influence degree of the specific external environmental factor on the direct drinking water quality and the sampling amount, for example, when the rainfall is large (as an external environmental factor), the water source will be more polluted, and the sampling amount needs to be increased to accurately monitor the water quality, at this time the environmental adjustment value corresponding to the rainfall will be larger; the normalization constant refers to a constant used for normalizing the calculation results, in the formula, it plays a role in adjusting the dimension and range of the calculation results, so that the sampling amount adjustment value (CZ) is within a reasonable numerical range.

[0112] The present application constructs the sampling optimization strategy of the direct drinking water sample based on the detection standard system and the sampling amount adjustment value, which can ensure the scientificity and accuracy of sampling, make the collected water sample truly reflect the quality of direct drinking water, reasonably allocate sampling resources, avoid unnecessary waste, improve sampling efficiency, help to discover water quality changes in time, and ensure the drinking safety of direct drinking water.

[0113] Among them, the sampling optimization strategy refers to a plan for comprehensively optimizing the collection process of direct drinking water samples based on the detection standard system applicable to direct drinking water samples and the sampling volume adjustment value calculated based on real-time flow parameters, including determining reasonable sampling time, sampling location, sampling frequency and sampling volume according to different time periods, different flow fluctuation stages, external environmental factors, etc., to ensure that the collected water samples are representative and can accurately reflect the water quality of direct drinking water. Optionally, the sampling optimization strategy for constructing the direct drinking water sample can be implemented through the LINGO tool, such as: taking sampling cost, sampling time, sampling volume, etc. as decision variables, and taking meeting the detection standards and accurately reflecting the water quality as constraints to construct a sampling optimization strategy.

[0114] The planning module 104 is used to perform topological processing on the layout of the delivery pipeline of the direct drinking water sample to obtain pipeline topology data, determine the optimal monitoring point corresponding to the direct drinking water sample based on the pipeline topology data, query the equipment installation parameters corresponding to the optimal monitoring point, and formulate a point layout plan corresponding to the water supply source area based on the equipment installation parameters.

[0115] The present invention obtains pipeline topology data by topologically processing the layout of the pipeline for conveying the direct drinking water sample, which helps to accurately grasp the connection method and direction of the pipeline, quickly locate the problem area during pipeline maintenance and troubleshooting, reduce water outage time and repair costs, optimize pipeline design, and rationally plan the laying of new pipelines based on the topological data to improve water supply efficiency. At the same time, it also provides a basis for the scientific arrangement of water quality monitoring points, thereby ensuring the quality and safety of direct drinking water during the transportation process.

[0116] Among them, the pipeline topology data refers to the data information that can reflect the structural characteristics of the pipeline network, which is extracted and sorted out after applying topological principles to analyze the key layout nodes and the entire transmission pipeline layout. It covers the numbering of key layout nodes, the connection relationship between nodes (which nodes are connected to each other, the order of connection, etc.), the weight of each connection link (equivalent to an abstract pipeline) (which can be set based on factors such as pipe diameter and water flow resistance), the connectivity of the entire pipeline network (whether there are isolated parts, how the various areas are connected, etc.) and loop information (whether there is a closed water circulation path, etc.).

[0117] As an embodiment of the present invention, the topological processing of the delivery pipeline layout of the direct drinking water sample to obtain pipeline topology data includes: obtaining the delivery water pipeline corresponding to the direct drinking water sample; querying the delivery pipeline layout corresponding to the delivery water pipeline; collecting basic layout information corresponding to the delivery pipeline layout; identifying key layout nodes in the basic layout information; and performing topological processing on the key layout nodes to obtain pipeline topology data.

[0118] The conveying water pipeline refers to the general term of various pipeline facilities for conveying direct drinking water samples, which covers pipelines of different diameters and materials (such as stainless steel pipes, PE pipes, etc.), which are connected to form a network from the water source or water treatment facilities to various water terminal ends; the conveying pipeline layout refers to the overall presentation of the distribution, arrangement and mutual connection of the conveying water pipeline in space, including the trend of each pipeline (such as straight line, bending, up and down running, etc.), the connection mode between each section of pipeline (direct connection, connection through pipe fittings, etc.), the specific position of different diameter pipelines (how deep below the ground, the specific floor and direction in the building, etc.), and the range covered by the pipeline network (which areas are involved, which water units are served, etc.); the basic layout information refers to the basic data collection around the conveying pipeline layout, including the starting and ending positions of the pipeline, the actual length of each section of the pipeline, the specific numerical value of the pipe diameter size, the specific type of the pipe material, the position coordinates of each connection point (such as tee, cross, elbow, etc.), the environment of the pipeline (outdoor buried, indoor exposed or along the wall, etc.) and the relative position relationship between adjacent pipelines, etc.; the key layout node refers to a specific position point with important connection, control, distribution or convergence function in the conveying pipeline layout, such as the starting point of the pipeline branch, which determines the distribution of water flow in different directions; the pipeline intersection point is the place where the water flow of multiple sections of pipelines converges or disperses; and the valve installation point can control the opening and closing of the water flow of the corresponding pipe section, adjust the flow, and the position of the connection and conversion of pipelines of different diameters, etc.

[0119] Further, the acquisition of the conveying water pipeline corresponding to the direct drinking water sample can be achieved by pipeline identification and tracking technology, such as radio frequency identification tag and the like; the query of the conveying pipeline layout corresponding to the conveying water pipeline can be achieved by layout query tools, such as ArcGIS, SQL and the like; the collection of the basic layout information corresponding to the conveying pipeline layout can be achieved by laser scanning measurement technology, such as using a ground three-dimensional laser scanner to scan and measure along the conveying pipeline to obtain the conveying pipeline layout; the identification of the key layout node in the basic layout information can be achieved by node analysis algorithm, such as identifying the key layout node by calculating the degree, betweenness centrality and closeness centrality (reciprocal of the average distance of the node to other nodes) and the like graph theory indexes; the topological processing of the key layout node can be achieved by topological algorithm, such as DFS, BFS and the like algorithm.

[0120] The application determines the best monitoring point corresponding to the monitoring of the direct drinking water sample based on the pipeline topological data, can accurately locate the key node for monitoring, makes the obtained water sample more representative, effectively reflects the water quality condition of the whole water supply system, optimizes the resource allocation, avoids monitoring at unnecessary positions, saves manpower, material resources and financial cost, helps to find the source and potential risk of water quality problems in time, and improves the control ability of the direct drinking water quality

[0121] The best monitoring point refers to the monitoring position point finally selected after comprehensively considering the pipeline topological data, potential monitoring area, regional sensitive unit and water quality sensitive index, which can most effectively and accurately reflect the overall water quality condition and change trend of the direct drinking water.

[0122] As an embodiment of the application, the determination of the best monitoring point corresponding to the monitoring of the direct drinking water sample based on the pipeline topological data comprises: extracting the key topological features in the pipeline topological data; screening the potential monitoring area corresponding to the direct drinking water sample based on the key topological features; analyzing the regional sensitive unit corresponding to the potential monitoring area; calculating the water quality sensitive index corresponding to the regional sensitive unit; and determining the best monitoring point corresponding to the monitoring of the direct drinking water sample based on the water quality sensitive index.

[0123] The key topological feature refers to a structural element with a key indicating effect on water quality monitoring extracted from the pipeline topological data, for example, a branch node of the pipeline, which represents the water flow splitting and converging situation, and the water flow of different water sources or different processing stages is mixed here, which can cause water quality changes, the potential monitoring area refers to the area range that needs to be monitored according to the key topological feature, and these areas are based on topological structure analysis and are judged as the sections where the water quality is easy to change or has an important influence on the overall water quality, such as the pipe sections near multiple branch nodes, due to the complex mixing of water flow, the water quality fluctuation is large, the area sensitive unit refers to the local unit that is more sensitive to water quality changes, which is further subdivided in the potential monitoring area, for example, in the curved part of the pipeline, the water flow form is complex, which is easy to cause the deposition and aggregation of substances, and the transition section connected with different material pipes can cause electrochemical corrosion or substance exudation due to the material difference, and the water quality sensitive index refers to a numerical index for quantitatively evaluating the water quality sensitivity of the area sensitive unit, which comprehensively considers various factors of the area sensitive unit, such as water flow velocity change, distance from the pollution source, stability of the pipe material, and interference degree of the surrounding environment.

[0124] Further, the extraction of the key topological feature in the pipeline topological data can be realized by a graph theory algorithm, such as using degree centrality, intermediate centrality and proximity centrality to identify the key topological feature, the screening of the potential monitoring area corresponding to the direct drinking water sample can be realized by a rule-based screening method, such as according to the pre-set rules, such as the pipe sections within a certain range from the key topological features (such as branch nodes, pipe diameter mutation points, etc.), the pipelines at the boundary of the specific water supply area, and the pipelines related to the historical water quality problem area, the potential monitoring area is screened out, the analysis of the area sensitive unit corresponding to the potential monitoring area can be realized by a fluid mechanics simulation tool, such as ANSYS Fluent and other tools, the calculation of the water quality sensitive index corresponding to the area sensitive unit can be realized by a weighted summation algorithm, such as a simple weighting, entropy weight method and other algorithms, and the determination of the best monitoring point corresponding to the direct drinking water sample can be realized by a multi-objective optimization algorithm, such as NSGA-II algorithm.

[0125] The application can ensure that the monitoring equipment is accurately adapted to the pipeline environment and water quality of the installation point by querying the equipment installation parameters corresponding to the best monitoring point, so as to guarantee the accuracy and reliability of the monitoring data, and can reasonably plan the equipment layout and installation mode according to the installation parameters, improve the installation efficiency, reduce the cost, and reduce the equipment failure and maintenance frequency caused by improper installation.

[0126] The device installation parameters refer to a series of technical indexes and condition information closely related to installation of water quality monitoring devices at optimal monitoring points, including specific position coordinates and height information of device installation to determine the accurate installation orientation on the pipeline; required space size, such as length, width and height, to ensure that there is a suitable physical space after device placement and the surrounding pipeline facilities and water flow are not affected; specifications and models of pipeline interfaces to ensure the adaptability and sealing of the connection between the monitoring device and the pipeline; device installation angle to enable the device to be in the optimal sampling and monitoring posture; and power supply parameters required for device operation, such as voltage, current and power. Optionally, the device installation parameters corresponding to the optimal monitoring points can be obtained by a database query-based method, such as by querying a preset device installation database that stores installation parameter information of different types of monitoring devices under various pipeline conditions and monitoring points.

[0127] Based on the device installation parameters, the point layout plan corresponding to the water supply source area is formulated, which can make the monitoring point layout scientific and reasonable, ensure that the water quality conditions of the water supply area are comprehensively and accurately reflected, potential water quality problems are detected in a timely manner, resource allocation is optimized, blind setting of monitoring points is avoided, unnecessary equipment and manpower investment is reduced, and monitoring efficiency is improved.

[0128] The point layout plan refers to a detailed planning scheme finally formulated according to the point optimization direction, which is used to guide the actual installation of monitoring devices in the water supply source area. The point layout plan clearly specifies the position coordinates of each monitoring device to be installed, the layout relationship between points (such as mutual distance and distribution form), the type and number of devices matched with each point, and the arrangement of power supply, communication connection, protection measures and other aspects related to device operation guarantee.

[0129] As an embodiment of the present application, based on the device installation parameters, the point layout plan corresponding to the water supply source area is formulated, including: performing feature extraction on the device installation parameters to obtain parameter feature information; querying a point layout set matched with the parameter feature information; integrating the location parameters of the water supply source area with the point layout set to obtain integrated layout data; analyzing the point optimization direction corresponding to the integrated layout data; and formulating the point layout plan corresponding to the water supply source area based on the point optimization direction.

[0130] The parameter characteristic information refers to the relevant characteristic description content related to point arrangement extracted from the equipment installation parameters, for example, the size specification (length, width, height) of the equipment reflects the size of the space it occupies, the space range required for installation and the spacing requirement with the surrounding environment, the specific way of equipment installation (such as pipe connection type, ground placement type, etc.), the adaptation characteristics to environmental conditions (such as temperature, humidity, waterproof, etc.), and the interface requirements related to power supply, data transmission, etc.; the point arrangement set refers to a series of point arrangement scheme sets that meet different equipment installation characteristic conditions, which are pre-planned, sorted or accumulated through experience, considering different equipment types, different geographical environments, etc. under various water supply scenarios; the position parameter refers to the parameter information representing the position, range and related geographical attributes of the water supply source area in the geographical space. It includes the geographical coordinates (longitude, latitude) of the area, the boundary range (length, width, etc. size information), the topography (mountain, plain or valley, etc.), and the relative position relationship of the surrounding landmark buildings, traffic lines, etc.; the integrated arrangement data refers to the data content generated by organically integrating the position parameter of the water supply source area with the point arrangement set, which not only integrates the layout ideas and point arrangement characteristics of each scheme in the point arrangement set, but also combines the actual geographical position, range, surrounding environment, etc. of the water supply source area; the point optimization direction refers to the direction guide for improving and adjusting the point arrangement determined after analyzing the integrated arrangement data, for example, according to the water flow direction and water source distribution of the water supply area, it is determined which point needs to be close to the key node of water flow (such as convergence point, flow dividing point) to better monitor water quality; considering the topography, underground facility distribution, etc. in the region.

[0131] Further, the feature extraction of the equipment installation parameters can be realized by feature engineering methods, such as principal component analysis PCA, feature selection algorithm, etc. The query and the parameter characteristic information matched point arrangement set can be realized by fuzzy matching algorithm, such as comparing the parameter characteristic information with the rules in the point arrangement set, and screening out the point arrangement set that meets the conditions most. The integration of the position parameter of the water supply source area and the point arrangement set can be realized by spatial analysis tools, such as ArcG IS, QGIS, etc. The analysis of the point optimization direction corresponding to the integrated arrangement data can be realized by data mining algorithm, such as clustering analysis K-means, analytic hierarchy process AHP, etc. The development of the point arrangement plan corresponding to the water supply source area can be realized by planning and design tools, such as AutoCAD, Revit, etc.

[0132] The scheme generation module 105 is configured to determine the monitoring target task corresponding to the direct drinking water sample based on the dynamic monitoring plan, the sampling optimization strategy and the monitoring point arrangement plan, execute the monitoring target task by using a preset direct drinking water online monitoring cloud platform, obtain task execution data, and generate an online monitoring scheme corresponding to the direct drinking water sample based on the task execution data.

[0133] Based on the dynamic monitoring plan, the sampling optimization strategy and the monitoring point arrangement plan, the monitoring target task corresponding to the direct drinking water sample is determined, so that the monitoring work is more systematic and targeted, the key links are accurately focused according to various aspects, the monitoring efficiency is improved, the monitoring data is ensured to be comprehensive and accurate, and the resource allocation is optimized.

[0134] The monitoring target task refers to the specific target to be achieved and the tasks to be performed when monitoring the direct drinking water sample, and specifically includes the detection requirements of various key water quality indicators (such as microbial indicators, chemical substance content, sensory property indicators, etc.) of the direct drinking water sample at different stages and different frequencies according to the dynamic monitoring plan; the operation details such as sampling time, sampling amount and sampling method at each monitoring point according to the sampling optimization strategy; and the monitoring focus and responsibility of each monitoring point according to the monitoring point arrangement plan, for example, a specific point focuses on water quality changes at the water source, and some points focus on water quality stability monitoring in the water supply process. Optionally, the determination of the monitoring target task corresponding to the direct drinking water sample can be realized by a task allocation algorithm, such as the Hungarian algorithm and the greedy algorithm, and detailed monitoring target tasks are generated according to the monitoring optimization direction.

[0135] Further, the monitoring target task is executed by using the preset direct drinking water online monitoring cloud platform to obtain task execution data, so that real-time and efficient data collection and transmission can be realized, the geographical and time limitations are broken, the timeliness and accuracy of the monitoring data are ensured, a large amount of task execution data can be quickly analyzed and integrated, the water quality dynamic change trend can be accurately understood, and the overall monitoring management level and emergency response efficiency are improved.

[0136] The preset direct drinking water online monitoring cloud platform is a comprehensive digital platform specially used for direct drinking water quality monitoring and constructed based on cloud computing technology, which integrates advanced sensing technology, data communication technology and intelligent analysis algorithm, has a wide data collection function, can receive a plurality of types of data transmitted by water quality monitoring equipment distributed at each monitoring point of the water supply network in real time, and covers physical, chemical, microbial and other water quality index information. The task execution data refers to that the execution of the monitoring target task can be realized by the preset direct drinking water online monitoring cloud platform.

[0137] Further, the present application generates an online monitoring scheme corresponding to the direct drinking water sample based on the task execution data, which can accurately optimize the monitoring process according to the actual monitoring situation, improve the pertinence and efficiency of monitoring, avoid resource waste in unnecessary links, and through in-depth analysis of data, potential risk points of water quality can be found in time, which helps to realize dynamic adjustment and adaptive optimization of the monitoring scheme, and ensures the continuous stability and safety of direct drinking water supply.

[0138] The online monitoring scheme refers to a systematic planning arrangement formulated based on the task execution data for real-time and continuous monitoring of direct drinking water samples, which covers the specific layout and adjustment strategy of monitoring points, clearly defines the key water quality indicators (such as microorganisms, chemical substance content, etc.) responsible for monitoring at each point and the corresponding monitoring frequency; specifies the type of monitoring equipment, technical parameters and operation and maintenance requirements to ensure stable and accurate data collection; also includes data transmission path, format and frequency setting, as well as water quality early warning mechanism based on data analysis, emergency handling process and other contents. Optionally, the generation of the online monitoring scheme corresponding to the direct drinking water sample can be realized through a scheme generation tool, such as Tableau, Power BI, etc.

[0139] First of all, the present invention obtains direct drinking water samples and identifies the water supply source area corresponding to the direct drinking water samples. It can implement targeted monitoring strategies based on the unique characteristics of different water supply areas, such as the water quality basis of the water source and the surrounding environmental conditions, improve monitoring accuracy, and help to quickly locate specific areas where water quality problems occur. When the problem first occurs, timely measures can be taken to effectively ensure water safety. The present invention detects the key component indicators in the water sample classification information and identifies the potential risk levels corresponding to the key component indicators. It helps to accurately locate the weak links in the quality of direct drinking water. According to the risk level, it can provide early warning of possible water quality deterioration, provide a basis for timely and targeted improvement measures, and enable resources to be allocated to the monitoring and processing of high-risk component indicators, effectively ensuring the stability and safety of direct drinking water supply and reducing health risks caused by water quality problems. The present invention constructs a detection standard system applicable to the direct drinking water samples, which can provide a clear and unified quality assessment for direct drinking water. With reference to, ensuring that water quality meets safety and health requirements, protecting the public's drinking water safety, helping to standardize the production and testing processes of water supply companies, improving the overall level of the industry, and promoting the healthy development of the direct drinking water industry, the present invention performs topological processing on the layout of the delivery pipeline of the direct drinking water sample to obtain pipeline topology data, which helps to accurately grasp the connection method and direction of the pipeline, and can quickly locate the problem area during pipeline maintenance and troubleshooting, reducing water outage time and repair costs, optimizing pipeline design, and rationally planning the laying of new pipelines based on topological data to improve water supply efficiency. At the same time, it also provides a basis for the scientific layout of water quality monitoring points to ensure the quality and safety of direct drinking water during transportation. Furthermore, based on the dynamic monitoring plan, sampling optimization strategy and monitoring point layout plan, the present invention determines the monitoring target tasks corresponding to the direct drinking water sample, which can make the monitoring work more systematic and targeted, accurately focus on key links based on various aspects of planning, improve monitoring efficiency, ensure that monitoring data is comprehensive and accurate, and help optimize resource allocation. Therefore, a direct drinking water quality online monitoring system and method proposed in the present invention can improve the online monitoring guarantee of direct drinking water quality.

[0140] like Figure 2 FIG. 1 is a flow chart of a method for online monitoring of drinking water quality according to an embodiment of the present invention. In this embodiment, the child lock management method based on data technology includes:

[0141] Obtaining a direct drinking water sample, identifying the water supply source area corresponding to the direct drinking water sample, analyzing the characteristics of the water users in the water supply source area and the surrounding environmental factors, and preliminarily classifying the direct drinking water sample based on the characteristics of the users and the environmental factors to obtain water sample classification information;

[0142] Detecting key component indicators in the water sample classification information, identifying potential risk levels corresponding to the key component indicators, determining the monitoring item priorities corresponding to the direct drinking water samples based on the potential risk levels, calculating monitoring frequency weights corresponding to the monitoring item priorities, and generating a dynamic monitoring plan corresponding to the water supply area based on the monitoring frequency weights;

[0143] Constructing a detection standard system applicable to the direct drinking water samples, analyzing the real-time flow parameters corresponding to the direct drinking water samples, calculating the sampling volume adjustment values ​​of the direct drinking water samples at different time periods based on the real-time flow parameters, and constructing a sampling optimization strategy for the direct drinking water samples based on the detection standard system and the sampling volume adjustment values;

[0144] Performing topological processing on the pipeline layout of the direct drinking water sample to obtain pipeline topology data, determining the optimal monitoring point corresponding to the direct drinking water sample based on the pipeline topology data, querying the equipment installation parameters corresponding to the optimal monitoring point, and formulating a point layout plan corresponding to the water supply source area based on the equipment installation parameters;

[0145] Based on the dynamic monitoring plan, sampling optimization strategy and monitoring point layout plan, the monitoring target task corresponding to the direct drinking water sample is determined, and the monitoring target task is executed using the preset direct drinking water online monitoring cloud platform to obtain task execution data. Based on the task execution data, an online monitoring plan corresponding to the direct drinking water sample is generated.

[0146] In the several embodiments provided by the present invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and actual implementation may employ other division methods.

[0147] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A direct drinking water quality on-line monitoring system, characterized in that, The system comprises a preliminary classification module, a monitoring planning module, a strategy construction module, a plan making module and a scheme generating module; The preliminary classification module is configured to obtain direct drinking water samples, identify water supply source areas corresponding to the direct drinking water samples, analyze water user population characteristics and surrounding environmental factors of the water supply source areas, wherein the water user population characteristics refer to comprehensive characteristics of water user population covering age structure, health status, occupation type, living habits and water use behavior mode, and the surrounding environmental factors refer to geographical and geomorphic features, land use types, industrial distribution, agricultural production activity intensity, residential population density and ecological environment status of water source and surrounding areas of the water supply source areas, and population characteristics and environmental characteristics in the water user population characteristics and the surrounding environmental factors are extracted respectively; Based on the population characteristics and the environmental characteristics, a feature mapping rule set corresponding to the direct drinking water samples is constructed, wherein the feature mapping rule set refers to a systematic rule set for establishing a connection between population characteristics and environmental characteristics and classification attributes of direct drinking water samples; Water sample feature groups in the feature mapping rule set are queried, wherein the water sample feature groups refer to a plurality of different sets formed by grouping direct drinking water samples with similar population characteristics and environmental characteristics combinations according to the feature mapping rule set; Water sample feature labels in the water sample feature groups are extracted; Based on the water sample feature labels, the direct drinking water samples are preliminarily classified to obtain water sample classification information; The monitoring planning module is configured to detect key component indicators in the water sample classification information, identify potential risk levels corresponding to the key component indicators, determine monitoring item priorities corresponding to the direct drinking water samples based on the potential risk levels, calculate monitoring frequency weights corresponding to the monitoring item priorities, and generate dynamic monitoring plans corresponding to the water supply source areas based on the monitoring frequency weights, wherein the dynamic monitoring plans refer to reasonable arrangements of monitoring items in time and resources in the water supply source areas according to the monitoring frequency weights; The strategy construction module is configured to construct a detection standard system applicable to the direct drinking water samples, analyze real-time flow parameters corresponding to the direct drinking water samples, calculate sampling amount adjustment values of the direct drinking water samples in different time periods based on the real-time flow parameters, and construct a sampling optimization strategy of the direct drinking water samples based on the detection standard system and the sampling amount adjustment values, wherein the sampling optimization strategy refers to reasonable sampling time, sampling location, sampling frequency and sampling amount determined according to different time periods, different flow fluctuation stages and external environmental factors; The plan making module is configured to perform topological processing on a layout of a conveying pipeline of the direct drinking water samples to obtain pipeline topological data, determine optimal monitoring points corresponding to the direct drinking water samples based on the pipeline topological data, query equipment installation parameters corresponding to the optimal monitoring points, and make a point arrangement plan corresponding to the water supply source areas based on the equipment installation parameters. The scheme generation module is configured to determine a monitoring target task corresponding to the direct drinking water sample based on the dynamic monitoring plan, the sampling optimization strategy, and the monitoring point arrangement plan, execute the monitoring target task by using a preset direct drinking water online monitoring cloud platform to obtain task execution data, and generate an online monitoring scheme corresponding to the direct drinking water sample based on the task execution data.

2. The on-line monitoring system for direct drinking water quality according to claim 1, wherein, The method further includes: dividing the potential risk level into intervals to obtain risk division intervals; quantifying the key component indicators of the direct drinking water sample to obtain index quantization values; matching the index quantization values to the risk division intervals to obtain risk index features; cross-analyzing the risk index features to obtain a risk correlation matrix; screening matrix correlation items in the risk correlation matrix; determining a monitoring item priority corresponding to the direct drinking water sample based on the matrix correlation items.

3. The online drinking water quality monitoring system according to claim 1, characterized in that: The method further includes: detecting component compositions corresponding to the direct drinking water sample; extracting core composition indices corresponding to the component compositions; querying extraction technical specifications corresponding to the core composition indices; determining standard extraction elements corresponding to the direct drinking water sample based on the extraction technical specifications; constructing a detection standard system applicable to the direct drinking water sample based on the standard extraction elements.

4. The on-line monitoring system for direct drinking water quality according to claim 1, wherein, The method further includes: obtaining a conveying water pipeline corresponding to the direct drinking water sample; querying a conveying pipeline layout corresponding to the conveying water pipeline; collecting basic layout information corresponding to the conveying pipeline layout; identifying key layout nodes in the basic layout information; topologically processing the key layout nodes to obtain pipeline topological data.

5. The on-line monitoring system for direct drinking water quality according to claim 1, wherein, The method further includes: extracting key topological features in the pipeline topological data; screening potential monitoring areas corresponding to the direct drinking water sample based on the key topological features; analyzing region-sensitive units corresponding to the potential monitoring areas; calculating water quality-sensitive indices corresponding to the region-sensitive units; determining a best monitoring point corresponding to the direct drinking water sample based on the water quality-sensitive indices.

6. The on-line monitoring system for direct drinking water quality according to claim 1, wherein, The method further includes: extracting parameter feature information from the equipment installation parameters; querying a point arrangement set matched with the parameter feature information; integrating location parameters of the water supply source area with the point arrangement set to obtain integrated arrangement data; analyzing a point optimization direction corresponding to the integrated arrangement data; developing a point arrangement plan corresponding to the water supply source area based on the point optimization direction.

7. A method for online monitoring of direct drinking water quality, characterized in that, A direct drinking water quality online monitoring system for executing any one of the methods in claims 1-6, the method comprising: The direct drinking water sample is obtained, the water supply source area corresponding to the direct drinking water sample is identified, the water user population characteristics and the surrounding environmental factors of the water supply source area are analyzed, the water user population characteristics refer to the comprehensive characteristics of the age structure, health status, occupation type, living habit and water use behavior mode of the water user population, and the surrounding environmental factors refer to the geographical and geomorphic characteristics, land use type, industrial distribution, agricultural production activity intensity, resident settlement degree and ecological environment status of the water source and the surrounding area of the water supply source area, and the population characteristics and environmental characteristics in the water user population characteristics and the surrounding environmental factors are extracted respectively; Based on the population characteristics and the environmental characteristics, a feature mapping rule set corresponding to the direct drinking water sample is constructed, wherein the feature mapping rule set refers to a systematic rule set for establishing a connection between the population characteristics and the environmental characteristics and the classification attributes of the direct drinking water sample; The water sample feature group in the feature mapping rule set is queried, wherein the water sample feature group refers to a plurality of different sets formed by grouping the direct drinking water samples with similar population characteristics and environmental characteristics combinations according to the feature mapping rule set; The water sample feature label in the water sample feature group is extracted; Based on the water sample feature label, the direct drinking water sample is preliminarily classified to obtain water sample classification information; Key component indicators in the water sample classification information are detected, potential risk levels corresponding to the key component indicators are identified, monitoring item priorities corresponding to the direct drinking water sample are determined based on the potential risk levels, monitoring frequency weights corresponding to the monitoring item priorities are calculated, and a dynamic monitoring plan corresponding to the water supply source area is generated based on the monitoring frequency weights, wherein the dynamic monitoring plan refers to a reasonable arrangement of time and resources for monitoring items in the water supply source area according to the monitoring frequency weights; A detection standard system suitable for the direct drinking water sample is constructed, real-time flow parameters corresponding to the direct drinking water sample are analyzed, sampling amount adjustment values of the direct drinking water sample in different time periods are calculated based on the real-time flow parameters, and a sampling optimization strategy of the direct drinking water sample is constructed based on the detection standard system and the sampling amount adjustment values, wherein the sampling optimization strategy refers to a reasonable sampling time, sampling location, sampling frequency and sampling amount determined according to different time periods, different flow fluctuation stages and external environmental factors; The layout of the direct drinking water sample conveying pipeline is topologically processed to obtain pipeline topological data, the best monitoring point corresponding to the direct drinking water sample is determined based on the pipeline topological data, the equipment installation parameters corresponding to the best monitoring point are queried, and a point arrangement plan corresponding to the water supply source area is developed based on the equipment installation parameters; Based on the dynamic monitoring plan, the sampling optimization strategy and the monitoring point arrangement plan, a monitoring target task corresponding to the direct drinking water sample is determined, the monitoring target task is executed by using a preset direct drinking water online monitoring cloud platform to obtain task execution data, and an online monitoring scheme corresponding to the direct drinking water sample is generated based on the task execution data.

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