Urban water environment quality evaluation and assessment method based on automatic monitoring network

Abstract

The invention belongs to the technical field of water resource monitoring, and particularly relates to an urban water environment quality evaluation and assessment method based on an automatic monitoring network, which comprises a water quality monitoring module, a parameter preprocessing module, a comprehensive quality calculation module, a water quality analysis module, a long-acting management module and a closed-loop optimization module. According to the method, parameter weights are dynamically distributed through ecological influence, evaluation deviation caused by traditional fixed weights is avoided, and the scientificity of water quality index calculation is improved; a monitoring-analysis-management and control-optimization whole process is closed-loop, and a long-acting management cycle is formed by combining hierarchical management and control such as III-class limited rectification, IV / V-class production halt management and quarterly strategy updating; a multi-level monitoring network architecture is adopted, real-time monitoring data, seasonal factors and public feedback are integrated, and data comprehensiveness and credibility are enhanced; a pollution hotspot map is matched through five-level water quality labels, a pollution source is quickly positioned, and differentiated treatment decisions are assisted.

Description

Technical Field

[0001] The present invention belongs to the field of water resource detection, and particularly relates to urban water environment quality evaluation and assessment, and specifically discloses an urban water environment quality evaluation and assessment method based on an automatic monitoring network. Background Art

[0002] Water quality testing is a core tool for balancing human needs and ecological protection, and it achieves closed-loop management of health protection, pollution prevention and control, and sustainable resource utilization through data-driven means.

[0003] Traditional water quality testing relies primarily on manual sampling and laboratory analysis. After collecting water samples at regular intervals, data is obtained using methods such as chemical titration (e.g., COD determination), instrumental analysis (e.g., atomic absorption spectroscopy for heavy metals), or biological monitoring (e.g., indicator species observation). This data is then evaluated against national standards using single factors or comprehensive indices. This technology suffers from shortcomings such as low sampling frequency (usually 1-2 times per month), poor data timeliness, low pollution source tracing efficiency, and reliance on manual investigation. Furthermore, the assessment model uses fixed weights, making it difficult to dynamically reflect regional ecological differences. Specifically, Existing water quality assessments mostly use fixed weights, ignoring the differences in ecological sensitivity across regions and seasons. For example, when the ecological impact of ammonia nitrogen intensifies during the dry season, traditional methods still calculate based on a fixed ratio, causing the assessment results to deviate from the actual pollution hazards. The traditional monitoring-governance chain is broken, and manual analysis is time-consuming. For example, rectification of Class III water quality requires more than 30 days. In addition, there is a lack of a dynamic strategy update mechanism, making it difficult to respond to sudden pollution incidents. The system relies on limited monitoring points, such as national monitoring stations on main rivers, ignoring tributaries and seasonal agricultural non-point source pollution. Public feedback is not incorporated into the data system, which can easily lead to monitoring blind spots. Existing technologies require manual investigation of pollution sources. For example, it takes more than two weeks to locate polluting enterprises in Class V water bodies, and the lack of hotspot maps leads to irrational allocation of governance resources.

[0004] Therefore, dynamic intelligent assessment, closed-loop governance mechanism, multi-source data fusion and precise traceability are needed to solve the above problems. Summary of the Invention

[0005] In view of this, the present invention proposes a method for evaluating and assessing the quality of urban water environment based on an automatic monitoring network. The method constructs a multi-level monitoring network through a water quality monitoring module to collect multi-dimensional parameters in real time. The preprocessing module cleans the abnormal data and classifies and calculates the index. The comprehensive calculation module dynamically allocates weights to generate a comprehensive water quality index. The analysis module divides the index into five-level labels, Class I-V, and generates a pollution source traceability map. The long-term management module implements hierarchical management and control, including routine inspections / time-limited rectification / production suspension management. Finally, the closed-loop optimization module combines seasonal factors with the quarterly update strategy of public feedback to achieve adaptive optimization of the monitoring network.

[0006] The purpose of the present invention can be achieved by the following technical solution: a method for evaluating and assessing urban water environment quality based on an automatic monitoring network, comprising a water quality monitoring module, a parameter preprocessing module, a comprehensive quality calculation module, a water quality analysis module, a long-term management module and a closed-loop optimization module, characterized in that it specifically includes the following steps: Multi-dimensional parameter collection: The water quality monitoring module uses a multi-level monitoring network architecture to collect various water quality parameters in real time and generate original monitoring data streams; Data preprocessing: The parameter preprocessing module is used to classify water quality parameters, clean the original data stream, remove outliers, and calculate the corresponding index for each classification; Comprehensive index calculation: Through the comprehensive quality calculation module, the weight of each parameter is dynamically assigned according to the ecological impact of each index to calculate the comprehensive water quality index; Water quality analysis: The water quality analysis module classifies the comprehensive water quality index and outputs five-level water quality labels with corresponding pollution source hotspot maps: Class I water quality is excellent, Class II water quality is good, Class III water quality is slightly polluted, and Class IV / V water quality is severely polluted; Tiered management and control: Through the long-term management module, differentiated management is carried out based on water quality levels. Regular inspections are initiated for Class II and above water quality, rectification within a specified period is initiated for Class III, and production suspension and ecological compensation mechanisms are triggered for Class IV / V. Assessment rules and penalty measures are also set for the management results. System adaptive optimization: Establish a system that includes seasonal factors, monitoring point performance evaluation and public supervision feedback, and update the weight strategy and monitoring network layout every quarter through a closed-loop optimization module.

[0007] Combining all the above technical solutions, the present invention has the following positive effects: 1. The present invention dynamically allocates parameter weights based on ecological impacts, avoiding the assessment bias caused by traditional fixed weights and improving the scientific nature of water quality index calculation.

[0008] 2. The present invention closes the entire process of monitoring-analysis-control-optimization, and combines hierarchical control such as Class III deadline rectification, Class IV / V production suspension management and quarterly strategy updates to form a long-term management cycle.

[0009] 3. The present invention adopts a multi-level monitoring network architecture to integrate real-time monitoring data, seasonal factors and public feedback to enhance the comprehensiveness and credibility of the data.

[0010] 4. The present invention matches the pollution hotspot map with five-level water quality labels to quickly locate pollution sources and assist differentiated governance decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Attachment Figure 1 This is a system block diagram of the present invention.

[0013] Attachment Figure 2 It is a step diagram of the present invention. DETAILED DESCRIPTION

[0014] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0015] See also Figure 1 As shown, this embodiment proposes a method for evaluating and assessing urban water environment quality based on an automatic monitoring network, which includes a water quality monitoring module, a parameter preprocessing module, a comprehensive quality calculation module, a water quality analysis module, a long-term management module and a closed-loop optimization module.

[0016] like Figure 2 As shown, this implementation step includes the following steps: Multi-dimensional parameter collection: The water quality monitoring module adopts a multi-level monitoring network architecture to collect various water quality parameters in real time and generate original monitoring data streams.

[0017] It should be noted that the multi-level monitoring network architecture of this embodiment is specifically as follows: Main monitoring layer: Multi-parameter water quality sensors are deployed along key sections of the urban water system to collect real-time data on turbidity, conductivity, temperature, pH, dissolved oxygen, COD, BOD, total nitrogen, total phosphorus, ammonia nitrogen, total coliform bacteria, and thermotolerant coliform bacteria. Supplementary monitoring layer: A heavy metal and toxic substance detection module is added downstream of the sewage outlet to support real-time identification of pollutants using spectral analysis; Mobile monitoring layer: Use unmanned boats equipped with portable detectors to dynamically re-measure blind spots and align them with fixed site data in time and space.

[0018] Data preprocessing: The water quality parameters are classified through the parameter preprocessing module, and the original data stream is cleaned and outliers are removed, and the corresponding index of each classification is calculated.

[0019] It should be noted that the water quality classification in the embodiment is specifically as follows: Basic physical and chemical parameters include: turbidity, conductivity, temperature, pH and dissolved oxygen.

[0020] It needs to be explained that turbidity characterizes the concentration and particle size distribution of suspended particles in water such as sediment, organic matter, and microorganisms, and reflects the transparency of the water body. Increased turbidity often indicates soil erosion, sewage discharge, or algae proliferation.

[0021] Conductivity reflects the total ion concentration in water, including inorganic salts, heavy metals, and other conductive substances. High conductivity often comes from industrial wastewater, agricultural runoff, or seawater intrusion.

[0022] Temperature directly affects the chemical reaction rate and biological metabolic activities of water bodies. Extreme temperatures can disrupt the balance of the ecosystem. For example, high temperatures accelerate the decomposition of organic matter, leading to hypoxia, while low temperatures inhibit the degradation ability of microorganisms.

[0023] The pH value measures the acidity and alkalinity of water. Normal natural water has a pH range. pH outside the range will damage the cell membrane function of aquatic organisms and affect the solubility of heavy metals.

[0024] Dissolved oxygen (DO) is the concentration of free oxygen in water and is a key indicator of the self-purification ability of water. When DO is lower than a certain concentration, fish will suffocate. If it is lower, it will trigger anaerobic reactions and produce toxic substances such as hydrogen sulfide.

[0025] It should be noted that organic pollution parameters include: COD and BOD.

[0026] It should be explained that COD is chemical oxygen demand, which characterizes the total amount of organic matter and reducing inorganic matter in water. It is expressed as the amount of oxygen required for strong oxidants such as potassium permanganate to oxidize pollutants. High COD indicates that the water body is polluted by industrial or domestic sewage.

[0027] BOD5 stands for biochemical oxygen demand, which refers specifically to the dissolved oxygen consumed by microorganisms to decompose organic matter within 5 days at 20°C. The BOD5 / COD ratio reflects the biodegradability of pollutants.

[0028] It should be noted that nutrient parameters include: ammonia nitrogen, total nitrogen and total phosphorus.

[0029] It should be explained that total nitrogen (TN) includes the total amount of ammonia nitrogen, nitrate, nitrite and organic nitrogen. Excessive nitrogen input, such as fertilizer loss, triggers algal blooms and causes eutrophication of water bodies.

[0030] Total phosphorus (TP) is the core driving factor of eutrophication in water bodies, mainly coming from detergents, agricultural runoff and organic waste. When the phosphorus concentration exceeds a certain range, it can trigger explosive growth of algae.

[0031] Ammonia nitrogen NH3-N is the sum of free ammonia and ammonium salts. When the concentration exceeds a certain value, it will have significant acute toxicity to fish and will consume dissolved oxygen through nitrification reaction.

[0032] It should be specified that toxicity parameters include: heavy metals and toxic substances.

[0033] It should be explained that heavy metals refer to the abnormal enrichment of metal elements or their compounds with a density ≥4.5 g / cm³ in water bodies, mainly including mercury (Hg), cadmium (Cd), lead (Pb), chromium (Cr), arsenic (As), etc. Although arsenic is a metalloid, it is often classified as a heavy metal due to its toxicity and polluting characteristics.

[0034] Toxic substances include organic poisons such as pesticides, polychlorinated biphenyls (PCBs), and radioactive substances in addition to heavy metals. It should be noted that the microbial risk parameters include: total coliform bacteria and thermotolerant coliform bacteria.

[0035] It needs to be explained that total coliform bacteria are indicator organisms of intestinal flora contamination, reflecting the risk of water contamination by feces or domestic sewage. Drinking water standards require that they must not be detected.

[0036] Thermotolerant coliform bacteria, also known as fecal coliform bacteria, are a subgroup of total coliform bacteria that can grow at 44.5°C and are more specific indicators of recent fecal contamination. Their presence is highly correlated with pathogenic bacteria such as Salmonella.

[0037] It should be specifically stated that the basic physical and chemical parameters generate the basic index as follows: ; Where L is the physical and chemical basic index, T i In order to convert the original values ​​of each parameter into dimensionless scores, the range method was used to eliminate the dimension differences within the range of 0-1, and the five types of parameters were processed separately, specifically: Positive indicators such as dissolved oxygen and pH, , negative indicators such as turbidity and conductivity, , where X i is the measured value, X min / X max The national standard limit lower / upper limit corresponding to the parameter, temperature index , T o is the optimal water temperature, T r It is an ecologically suitable range.

[0038] W i The objective weight is determined by the entropy weight method combined with the seasonal adjustment factor. , where the entropy value , the data of m parameters and n sites collected are normalized to obtain the normalized matrix, and each parameter is normalized to calculate the probability matrix p of all parameters ij , p ij is the proportion of the standardized value of the jth sample of the i-th parameter.

[0039] For example, assuming the entropy value E i The turbidity is 0.905, the conductivity is 0.842, the temperature is 0.936, the pH value is 0.798, and the dissolved oxygen is 0.877. Calculate 1−E i : Turbidity: 1-0.905=0.095; Conductivity: 1-0.842=0.158; Temperature: 1-0.936=0.064; pH: 1-0.798=0.202; Dissolved oxygen: 1-0.877=0.123; Sum: 0.095 + 0.158 + 0.064 + 0.202 + 0.123 = 0.642; The base value weights of each parameter were obtained: turbidity: 0.095 / 0.642≈0.148; conductivity: 0.158 / 0.642≈0.246; temperature: 0.064 / 0.642≈0.100; pH: 0.202 / 0.642≈0.315; dissolved oxygen: 0.123 / 0.642≈0.192.

[0040] Superimposed seasonal factor S f Assuming it is summer, the summer weight coefficients are used: turbidity*1.5, conductivity*0.9, temperature*1.1, pH*1.0, dissolved oxygen*1.2 to obtain the final weights, and verify whether the sum of the weights is 1. If not, the weights need to be renormalized in actual applications.

[0041] It can be seen that the larger the entropy value is, the closer it is to 1, the lower the degree of data dispersion, the less information the parameter provides, and the lower the weight. Here, the entropy value of pH is the lowest, indicating that its data difference is the largest and the weight is the highest.

[0042] It should be noted that the organic pollution parameters used to generate the organic pollution index are as follows: ; Where R is the organic pollution index, COD is the chemical oxygen demand, which represents the total organic pollutant content in water, including degradable and non-degradable parts; S COD Chemical oxygen demand (COD) refers to the amount of oxygen required for the complete oxidation of organic matter dissolved in water without suspended matter by a strong oxidant, reflecting the content of organic matter that can be rapidly degraded by microorganisms. BOD is biochemical oxygen demand, which reflects the oxygen consumption of biodegradable organic matter and is directly related to microbial activity; S BODDissolved biochemical oxygen demand (BOD) refers to the amount of dissolved oxygen consumed by dissolved organic matter during the biochemical degradation process. It characterizes the pollution load of biodegradable dissolved organic matter. It is usually measured as the five-day BOD S BOD5 ; COD / S COD and BOD / S BOD The ratio is standardized, the COD / BOD ratio indicates the biodegradability of sewage, and a ratio exceeding a certain number indicates a high proportion of difficult-to-degrade organic matter; the numerator represents the total amount of comprehensive pollution and biodegradability, and the denominator is the balance of the three dimensional differences.

[0043] It should be noted that the nutrient parameters used to generate the eutrophication index are as follows: ; Where Y is the nutrient index, TN represents the total input of nitrogen, including organic nitrogen and inorganic nitrogen, which dominates the long-term nutrient status of the water body; S TN It is the standardized reference value of total nitrogen pollution load, reflecting the carrying threshold of water bodies to nitrogen pollution; therefore, its ratio has the highest weight, ranging from 0.35 to 0.4; ‌TP‌ Total phosphorus is the key limiting factor controlling eutrophication and indicates eutrophication risk; S TN It is the standardized reference concentration for total phosphorus pollution assessment, which directly affects the determination of eutrophication risk level; its ratio weight is between 0.3-0.35; NH3-N ammonia nitrogen is a highly toxic form of nitrogen, reflecting the short-term pollution load of reduced nitrogen in water bodies; S NH3-N The reference standard concentration used to measure the degree of ammonia nitrogen pollution is usually taken from national or industry water quality standards; its ratio has the same weight as total phosphorus, ranging from 0.3-0.35; Each of the above ratios is its normalized value, and the weight is allocated according to the promoting effect of each parameter on algae growth.

[0044] It should be noted that the toxicity parameters used to generate the comprehensive toxicity index are: ; Where D is the comprehensive toxicity index, C i is the measured concentration of the i-th heavy metal, S i is the standard limit of heavy metal type i, C i / S i It represents the pollution index of the i-th heavy metal. A value greater than 1 indicates that the heavy metal exceeds the standard, and a value less than 1 indicates that it exceeds the standard. The square root mean square method is used to amplify the weight of high pollution factors and avoid the situation where a single pollutant exceeds the standard and masks the contribution of other pollution to quantify the overall heavy metal pollution intensity.

[0045] THC is the hazard coefficient of toxic substances, which compresses the order of magnitude difference of high toxicity values ​​and retains the effects of low toxicity substances. Logarithmic transformation is used to reduce the deviation of extreme toxicity values.

[0046] For example, the lead pollution index of a certain place is 2.1, the cadmium pollution index is 3.4, the pollution index is 1.8, and the organic toxic hazard coefficient is 420. The specific calculation process is: ; It should be noted that the microbial risk parameter generates the microbial contamination index as follows: ;

[0047] Where S is the microbial contamination index, C z is the measured concentration of total coliform bacteria, S z is the standard limit of total coliform bacteria; C n is the measured concentration of thermotolerant coliform bacteria, indicating recent fecal contamination, S n This is the standard limit for thermotolerant coliform bacteria; α is the weight coefficient of heat-resistant coliform bacteria, which ranges from 1.1 to 1.2. Because it has a higher priority for health risks and a higher weight, the square root of the sum of squares in the formula strengthens the contribution of high-pollution parameters.

[0048] Comprehensive index calculation: Through the comprehensive quality calculation module, the weights of each parameter are dynamically assigned according to the ecological impact of each index to calculate the comprehensive water quality index.

[0049] It should be noted that the comprehensive water quality index is specifically: ; Among them, Q is the comprehensive water quality index, and L is the physical and chemical basic index. Since the physical and chemical basic index reflects the basic properties of the water body, its parameters are the basic parameters for water quality evaluation, and its weight is between 0.25-0.3; R is the organic pollution index, which is directly related to the self-purification capacity of water bodies and the risk of oxygen depletion. Its weight is the same as that of the physical and chemical parameters, and is also between 0.25 and 0.3. Y is the nutrient index. The enrichment of this parameter is likely to cause algal blooms, but it has large regional differences and a slightly lower weight, ranging from 0.2 to 0.25. D is the comprehensive toxicity index, S is the microbial contamination index, the harm is direct but often local or seasonal, and has the lowest weight, between 0.1 and 0.15; This formula amplifies the contribution of high-pollution parameters through square operations. The weights are allocated according to the classification management principles of water quality standards and public health risks. The allocation principles reflect the priority of different pollution types and have dynamic adaptability that can be adjusted with the seasons. For example, microorganisms are more likely to reproduce in the rainy season, which increases the microbial risk weight.

[0050] Water quality analysis: The water quality analysis module classifies the comprehensive water quality index into different levels, and outputs five-level water quality labels with corresponding pollution source tracing hotspot maps: Class I water quality is excellent, Class II water quality is good, Class III is slightly polluted, and Class IV / V is severely polluted.

[0051] The water quality analysis is specifically as follows: When Q≤x, the water quality grade is Class I, indicating excellent water quality; When x<Q≤y, the water quality grade is Class II, indicating good water quality; When y<Q≤z, the water quality grade is Class III, indicating slight pollution; When Q>z, the water quality grade is IV / V, indicating severe pollution.

[0052] The value range of x is between 1-1.1, the value range of y is between 1.1-2, and the value range of z is between 2-4. The value of each value is set based on a combination of large-scale model analysis of sample data and manual experience to enter and store data. Appropriate adjustments can also be made based on seasonal or common sense influencing conditions.

[0053] Gradual management and control: Through the long-term management module, differentiated management is carried out based on the water quality level. Regular inspections are initiated for Class II and above water quality, rectification within a time limit is initiated for Class III, and production suspension and ecological compensation mechanism are triggered for Class IV / V. Assessment rules and punishment measures are set for the management results.

[0054] It should be noted that for water quality of Class II and above, monthly routine inspections covering basic indicators such as pH value and dissolved oxygen are carried out, combined with data comparison from automatic monitoring stations, and manual review is triggered when abnormal fluctuations exceed a certain value.

[0055] Class III water quality: Complete rectification within a specified time limit, simultaneously initiate pollution source tracing, and require cross-regional coordinated inspections through a joint prevention and control system. If standards are not met within the specified time limit, the control measures will be upgraded to Class IV.

[0056] ‌Class IV / V water quality‌: Immediately stop production and activate the ecological compensation mechanism, such as the calculation of compensation for upstream and downstream in the basin. The treatment plan must include a third-party assessment report. After rectification, production can only resume after meeting the standards for several consecutive quarters.

[0057] It needs to be explained that the linkage between assessment and punishment specifically means incorporating the water quality compliance rate into the performance assessment of local governments. For example, financial rewards will be given to the top three regions in terms of improvement, and a "blacklist" will be publicized and credit penalties will be imposed on enterprises that repeatedly exceed the standards.

[0058] System adaptive optimization: Establish a system that includes seasonal factors, monitoring point performance evaluation and public supervision feedback, and update the weight strategy and monitoring network layout every quarter through a closed-loop optimization module.

[0059] It should be noted that weights are adjusted every quarter based on comprehensive seasonal factors. For example, the weight of total phosphorus is increased during the flood season; stations where the data completeness rate of monitoring points is lower than the set value need to calibrate the weights of various parameters; and hotspots of public complaints are marked in high-incidence areas, and the assessment model parameters and monitoring network density are adjusted comprehensively.

[0060] It is important to note that the adaptive optimization process adopts a "monitor-analyze-decide-verify" cycle, specifically: Data integration: Access to meteorological, hydrological and other multi-source data to identify agricultural non-point source pollution risks during droughts; Performance evaluation: Eliminate monitoring points where the data abnormality rate exceeds a certain value for several consecutive months; Public Participation: A reporting channel for black and odorous water bodies will be opened, and complaints will be responded to within 48 hours after verification and included in the governance priority assessment.

[0061] Through the description of the above embodiments, those skilled in the art can clearly understand that the various embodiments of the present application can be implemented by means of software or software combined with a necessary general hardware platform, and of course can also be implemented by hardware functions; based on such understanding, the technical solution of the present application can essentially be embodied in the form of a software product or the part that contributes to the prior art. The software product is stored in a storage medium and includes a number of instructions for enabling a computer device, such as but not limited to a personal computer, a server, or a network device, to execute all or part of the steps of the method described in any embodiment of the present application.

[0062] The above describes exemplary embodiments of the present application. It should be understood that the above exemplary embodiments are not restrictive but illustrative, and the scope of protection of the present application is not limited thereto. It should be understood that those skilled in the art can modify and vary the embodiments of the present application without departing from the spirit and scope of the present application, and these modifications and variations should be within the scope of protection of the present application.

Claims

1. A method for evaluating and assessing urban water environment quality based on an automatic monitoring network, comprising a water quality monitoring module, a parameter preprocessing module, a comprehensive quality calculation module, a water quality analysis module, a long-term management module, and a closed-loop optimization module, characterized in that: The specific steps include: Multi-dimensional parameter collection: The water quality monitoring module uses a multi-level monitoring network architecture to collect various water quality parameters in real time and generate original monitoring data streams; Data preprocessing: The parameter preprocessing module is used to classify water quality parameters, clean the original data stream, remove outliers, and calculate the corresponding index for each classification; Comprehensive index calculation: Through the comprehensive quality calculation module, the weight of each parameter is dynamically assigned according to the ecological impact of each index to calculate the comprehensive water quality index; Water quality analysis: The water quality analysis module classifies the comprehensive water quality index and outputs five-level water quality labels with corresponding pollution source hotspot maps: Class I water quality is excellent, Class II water quality is good, Class III water quality is slightly polluted, and Class IV / V water quality is severely polluted; Tiered management and control: Through the long-term management module, differentiated management is carried out based on water quality levels. Regular inspections are initiated for Class II and above water quality, rectification within a specified period is initiated for Class III, and production suspension and ecological compensation mechanisms are triggered for Class IV / V. Assessment rules and penalty measures are also set for the management results. System adaptive optimization: Establish a system that includes seasonal factors, monitoring point performance evaluation and public supervision feedback, and update the weight strategy and monitoring network layout every quarter through a closed-loop optimization module.

2. The method for evaluating and assessing urban water environment quality based on an automatic monitoring network according to claim 1, wherein: The multi-level monitoring network architecture is specifically as follows: Main monitoring layer: Multi-parameter water quality sensors are deployed along key sections of the urban water system to collect real-time data on turbidity, conductivity, temperature, pH, dissolved oxygen, COD, BOD, total nitrogen, total phosphorus, ammonia nitrogen, total coliform bacteria, and thermotolerant coliform bacteria. Supplementary monitoring layer: A heavy metal and toxic substance detection module is added downstream of the sewage outlet to support real-time identification of pollutants using spectral analysis; Mobile monitoring layer: Use unmanned boats equipped with portable detectors to dynamically re-measure blind spots and align them with fixed site data in time and space.

3. The method for evaluating and assessing urban water environment quality based on an automatic monitoring network according to claim 1, wherein: The water quality classification is specifically as follows: Basic physical and chemical parameters include: turbidity, conductivity, temperature, pH value and dissolved oxygen; organic pollution parameters include: COD, BOD; nutrient parameters include: ammonia nitrogen, total nitrogen and total phosphorus; toxicity parameters include: heavy metals and toxic substances; microbial risk parameters include: total coliform bacteria and heat-resistant coliform bacteria.

4. The method for evaluating and assessing urban water environment quality based on an automatic monitoring network according to claim 3, wherein: The basic physical and chemical parameters generate the basic index specifically as follows: ； Where L is the physical and chemical basis index, and T i The original values ​​of each parameter are converted into dimensionless scores in the range of 0-1. i It is an objective weight determined by the entropy weight method combined with the seasonal adjustment factor; The organic pollution parameters used to generate the organic pollution index are specifically: ； Where R is the organic pollution index, COD is the chemical oxygen demand, S COD is dissolved chemical oxygen demand, BOD is biochemical oxygen demand, S BOD is the dissolved biochemical oxygen demand, and the denominator is the difference of the three dimensions of the balance; The nutrient parameters used to generate the eutrophication index are specifically: ； Where Y is the nutrient index, TN is the total nitrogen that dominates the long-term nutrient status of the water body, S is the total nitrogen that dominates the long-term nutrient status of the water body, TN为 Standardized benchmark value of total nitrogen, TP and total phosphorus are eutrophication risks, S TN is the standardized baseline value of total nitrogen. NH3-N is a highly toxic form of nitrogen, reflecting acute pollution. NH3-N is the standardized baseline value of ammonia nitrogen, and the weights are allocated according to the promoting effect of each parameter on algal growth; The toxicity parameters used to generate the comprehensive toxicity index are specifically: ； Where D is the comprehensive toxicity index, C i is the measured concentration of a single factor, S i is the corresponding standard limit for heavy metals, and THC is the hazard coefficient for toxic substances; The microbial risk parameter generates the microbial contamination index specifically as follows: ； Where S is the microbial contamination index, C z is the measured concentration of total coliform bacteria, S z is the standard limit of total coliform bacteria, C n is the measured concentration of thermotolerant coliform bacteria, S n is the standard limit of thermotolerant coliform bacteria, and α is the weight coefficient of thermotolerant coliform bacteria, because it has a higher priority for sanitary risks and has a higher weight.

5. The method for evaluating and assessing urban water environment quality based on an automatic monitoring network according to claim 1, wherein: The comprehensive water quality index is specifically: ； Among them, Q is the comprehensive water quality index, L is the physical and chemical basic index, R is the organic pollution index, Y is the nutrient index, D is the comprehensive toxicity index, and S is the microbial pollution index. The weights are allocated according to the classification management principles of water quality standards and public health risks.

6. The method for evaluating and assessing urban water environment quality based on an automatic monitoring network according to claim 1, wherein: The water quality analysis is specifically as follows: When Q≤x, the water quality grade is Class I, indicating excellent water quality; When x<Q≤y, the water quality grade is Class II, indicating good water quality; When y<Q≤z, the water quality grade is Class III, indicating slight pollution; When Q>z, the water quality grade is IV / V, indicating severe pollution.

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