Big data-based sewage monitoring data classification evaluation method

Abstract

The invention discloses a data classification evaluation method for sewage monitoring based on big data, and relates to the technical field of data processing. The data classification evaluation method for sewage monitoring based on big data comprises the following steps: dividing a total sewage monitoring area; performing concentration threshold contrastive analysis on the original data; performing second classification adjustment according to the second comparative analysis result; and performing third classification adjustment according to the third comparative analysis result. According to the method, first classification evaluation and second classification evaluation are performed on different data acquisition point locations according to an early warning result; performing second classification adjustment according to the second comparative analysis result; according to the third comparative analysis result, third classification adjustment is carried out, the effect of improving the accuracy of classification evaluation processing of the original data of sewage monitoring is achieved, and the problem that in the prior art, the accuracy of classification evaluation processing of the original data of sewage monitoring based on big data is insufficient is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data classification and evaluation method for sewage monitoring based on big data. Background Art

[0002] With the increasing severity of the problem, the use and preparation of not only violate the law, but also cause great harm to personal health and social security. In order to effectively combat crime and protect social public security, new monitoring and prevention means are actively explored. Among them, a data classification and evaluation method for sewage monitoring based on big data has emerged as the times require.

[0003] The existing data classification and evaluation of sewage monitoring based on big data are achieved through the following methods: sewage sampling technology to collect sewage samples from sewage treatment plants or specific areas; chemical analysis technology to detect the components in sewage using chemical analysis technologies such as high performance liquid chromatography and mass spectrometry; big data analysis technology to store, process and analyze the detected data through a big data platform to achieve data classification and evaluation.

[0004] For example, a drug situation monitoring system disclosed in the invention patent with the publication number of CN119046858A includes: a data acquisition model, a data preprocessing model, a drug situation analysis model, an anomaly analysis model and a data display model. In the drug situation analysis model, the data analysis layer uses a clustering method to obtain the input data of each index in the index layer. The index layer analyzes each index according to the input data to obtain the output data of each index. The comprehensive evaluation layer is used to comprehensively analyze the output data of each index, the weights of each index and the preprocessed data to obtain a comprehensive evaluation result. The anomaly analysis model is used to analyze the output data of each index and the preprocessed data in the drug situation analysis model to obtain an anomaly analysis report. The data display model visually displays the comprehensive evaluation result and the anomaly analysis report.

[0005] For example, a real-time visualization monitoring method for drug abuse disclosed in the invention patent with the publication number of CN111339172A includes: collecting influent samples and analyzing the concentration information based on the principle of sewage epidemiology; establishing a per capita drug abuse amount model to calculate the per capita drug abuse quantity, and constructing an epidemic rate model for such substances on the basis of continuously collecting and measuring sewage samples and metabolites thereof and combining the survey results of drug abuse frequency and dosage, so as to predict the number of drug abusers, the epidemic rate and the total amount of drug abuse in real time and establish a big database; reading the information in the big database, importing the data source into a visualization system, performing ETL processing on the data source to form a data warehouse, and visually expressing the data in various component forms on a web interface through front-end integration analysis.

[0006] However, in the process of implementing the technical solutions of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems: In the prior art, although significant progress has been made in data detection in sewage drug testing technology, the existing technology lacks a systematic analysis and early warning mechanism in the data collection and preliminary analysis stages. In addition, the classification evaluation generation process often relies on manual operations, which is not only inefficient but also difficult to ensure the consistency and accuracy of the classification evaluation, resulting in insufficient accuracy in the classification evaluation and processing of the original data of sewage monitoring based on big data. Summary of the Invention

[0007] By providing a data classification evaluation method for sewage monitoring based on big data in an embodiment of the present application, the problem of insufficient accuracy in the classification evaluation and processing of the original data of sewage monitoring based on big data in the prior art is solved, and the effect of improving the accuracy of the classification evaluation and processing of the original data of sewage monitoring is achieved.

[0008] An embodiment of the present application provides a data classification evaluation method for sewage monitoring based on big data, including the following steps: dividing the total sewage monitoring area to obtain different sewage monitoring sub-areas, and obtaining the original data at the data collection points in the sewage monitoring sub-areas from the database; performing a concentration threshold comparison analysis on the original data, giving an early warning according to the result of the concentration threshold comparison analysis, and performing a first classification evaluation and a second classification evaluation on different data collection points respectively according to the early warning result; performing a first comparison analysis according to the first classification evaluation result, performing a first classification adjustment according to the result of the first comparison analysis, performing a second comparison analysis according to the second classification evaluation result, and performing a second classification adjustment according to the result of the second comparison analysis; obtaining a comprehensive classification evaluation result according to the first classification evaluation result and the second classification evaluation result, performing a third comparison analysis according to the comprehensive classification evaluation result, and performing a third classification adjustment according to the result of the third comparison analysis.

[0009] Further, the giving an early warning according to the result of the concentration threshold comparison analysis specifically includes: classifying the original data according to predefined data categories to obtain the original data under different categories; if the original data under different categories is equal to or greater than the corresponding concentration threshold, recording the data collection points in the corresponding sewage monitoring sub-areas as first-level early warning points and notifying relevant personnel; if the original data under different categories is less than the corresponding concentration threshold, recording the data collection points in the corresponding sewage monitoring sub-areas as second-level early warning points.

[0010] Further, the performing a first classification evaluation and a second classification evaluation on different data collection points respectively according to the early warning result specifically includes: obtaining the medical waste data corresponding to the data collection points through the medical waste database of the sewage monitoring sub-area, and performing a first classification evaluation according to the original data and the medical waste data at the data collection points; if the data collection point is recorded as a second-level early warning point, performing a second classification evaluation according to the original data at the data collection point.

[0011] Further, the first classification evaluation is performed based on the original data and medical waste data at the data collection points, which specifically includes: sequentially numbering the data collection points, sequentially numbering the types of medical waste in the medical waste data, and sequentially numbering the monitoring time periods corresponding to the original data; directly extracting the medical concentration data, concentration detection threshold, sewage standard temperature, and sewage standard pH value from the medical waste database in the sewage monitoring sub-region; collecting and extracting the sewage temperatures of different types of medical waste at different data collection points and different monitoring time periods from the temperature collection device; collecting and extracting the sewage pH values of different types of medical waste at different data collection points and different monitoring time periods from the pH value collection device; comprehensively analyzing the medical concentration data, concentration detection threshold, sewage standard temperature, sewage standard pH value, sewage temperatures of different types of medical waste at different data collection points and different monitoring time periods, and sewage pH values of different types of medical waste at different data collection points and different monitoring time periods to obtain the concentration correction value.

[0012] Further, the first classification adjustment is performed according to the first comparative analysis result, which specifically includes: if the concentration correction value of a first-level warning point is equal to or greater than the concentration correction threshold, then mark the corresponding first-level warning point as a special-level warning point and notify relevant personnel to conduct on-site detection of the sewage monitoring sub-region where the special-level warning point is located; if the concentration correction value of a first-level warning point is less than the concentration correction threshold, then still mark the corresponding first-level warning point as a first-level warning point and increase the original data collection frequency of the corresponding first-level warning point; if the concentration correction value of a second-level warning point is equal to or greater than the concentration correction threshold, then mark the corresponding second-level warning point as a first-level warning point, increase the original data collection frequency of the corresponding second-level warning point, and perform local abnormal factor detection algorithm processing on the original data of the corresponding second-level warning point; if the concentration correction value of a second-level warning point is less than the concentration correction threshold, then mark the corresponding second-level warning point as a third-level warning point, and perform similar data cleaning on the original data of the corresponding third-level warning point through a fuzzy matching algorithm.

[0013] Further, the first classification adjustment based on the first comparative analysis result further includes: counting the number of first-level warning points, the number of second-level warning points, and the number of third-level points in the sewage monitoring sub-region; if the number of first-level warning points in the sewage monitoring sub-region is equal to or greater than the first-level warning point number threshold, all points in the corresponding sewage monitoring sub-region are recorded as special-level warning points; if the number of first-level warning points in the sewage monitoring sub-region is less than the first-level warning point number threshold, no adjustment is made; if the number of second-level warning points in the sewage monitoring sub-region is equal to or greater than the second-level warning point number threshold, all points in the corresponding sewage monitoring sub-region are recorded as first-level warning points; if the number of second-level warning points in the sewage monitoring sub-region is less than the second-level warning point number threshold, no adjustment is made; if the number of third-level warning points in the sewage monitoring sub-region is equal to or greater than the third-level warning point number threshold, all points in the corresponding sewage monitoring sub-region are recorded as second-level warning points; if the number of third-level warning points in the sewage monitoring sub-region is less than the third-level warning point number threshold, no adjustment is made.

[0014] Further, the second classification evaluation based on the original data at the data collection points specifically includes: directly extracting the sewage flow standard value, rainfall standard value, and concentration standard value from the medical waste database in the sewage monitoring sub-region; collecting and extracting the concentrations of different types at different second-level warning points during different monitoring time periods from the intelligent flow statistics device; collecting and extracting the sewage flow and rainfall at different second-level warning points during different monitoring time periods from the intelligent flow statistics device; comprehensively analyzing the sewage flow standard value, rainfall standard value, concentration standard value, the concentrations of different types at different second-level warning points during different monitoring time periods, and the sewage flow and rainfall at different second-level warning points during different monitoring time periods to obtain the concentration mutation degree value.

[0015] Further, the second classification adjustment based on the second comparative analysis result specifically includes: if the concentration mutation degree value of the second-level warning point is equal to or greater than the concentration mutation degree threshold, the corresponding second-level warning point is recorded as a first-level warning point, the original data collection frequency of the corresponding first-level warning point is increased, and relevant personnel are notified to perform data cleaning on the original data of the corresponding first-level warning point; if the concentration mutation degree value of the second-level warning point is less than the concentration mutation degree threshold, the corresponding second-level warning point is still recorded as a second-level warning point.

[0016] Further, obtaining the comprehensive classification evaluation result based on the first classification evaluation result and the second classification evaluation result specifically includes: comprehensively analyzing the concentration correction value and the concentration mutation degree value of the second-level warning point to obtain the comprehensive evaluation value of the concentration data of the second-level warning point, which is used to quantitatively evaluate the comprehensive level of the concentration data monitoring quality; numbering the second-level warning points in sequence. Indicates the number of the secondary warning points, , indicates the total number of the secondary warning points; the specific comprehensive analysis formula is as follows: ; Indicates the comprehensive evaluation value of the concentration data of the th data acquisition point, which is used to quantify the comprehensive evaluation value of the concentration data affected by interference in concentration monitoring; Indicates the concentration correction value of the th data acquisition point, which is used to quantify the relative negative degree value of the concentration monitoring affected by interference; Indicates the concentration mutation degree value of the th secondary warning point, which is used to quantify the relative level value of the concentration monitoring mutation degree; Indicates the sewage dissolved oxygen content influence correction factor of the th secondary warning point, which is used to quantify the relative level value of the concentration monitoring mutation degree, Indicates the weight factor of the concentration correction value of the th secondary warning point for the comprehensive evaluation value of the concentration data, Indicates the weight factor of the concentration mutation degree value of the

[0017] Furthermore, the third classification adjustment according to the third comparative analysis result specifically includes: if the comprehensive evaluation value of the concentration data of the secondary warning point is less than the concentration data comprehensive evaluation threshold, the original data of the corresponding secondary warning point is subjected to data cleaning through the standard score algorithm; if the comprehensive evaluation value of the concentration data of the secondary warning point is equal to or greater than the concentration data comprehensive evaluation threshold, the corresponding secondary warning point is marked as a primary warning point and the original data acquisition frequency of the corresponding secondary warning point is increased; the special warning points, primary warning points, secondary warning points, and tertiary warning points among all the data acquisition points in the sewage monitoring sub-region are used to generate an electronically mapped report with classification labels through the report generation model.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The first classification evaluation and the second classification evaluation are respectively performed on different data acquisition points according to the warning results; the second classification adjustment is performed according to the second comparative analysis result; the third classification adjustment is performed according to the third comparative analysis result, achieving the effect of improving the accuracy of the classification evaluation and processing of the original data of sewage monitoring, and solving the problem of insufficient accuracy in the classification evaluation and processing of the original data of sewage monitoring based on big data in the prior art.

[0019] 2. Conduct the first comparative analysis based on the first classification evaluation result, and perform the first classification adjustment according to the first comparative analysis result. By considering various interference factors, the actual concentration can be more accurately reflected, false alarms and missed reports can be reduced, and the influence of environmental factors such as temperature and pH value on detection can be considered, so that the detection result is more in line with the actual environmental conditions, and the real-time nature of data classification evaluation is thus maintained.

[0020] 3. Obtain the comprehensive classification evaluation result based on the first classification evaluation result and the second classification evaluation result, conduct the third comparative analysis according to the comprehensive classification evaluation result, and perform the third classification adjustment according to the third comparative analysis result. By comprehensively analyzing the concentration correction value and the mutation degree value, the monitoring quality of concentration data can be more comprehensively evaluated, so as to quantitatively evaluate the quality level of concentration data monitoring, provide an objective and operable basis for decision-making, and ensure the reliability of the original data. Brief Description of the Drawings

[0021] Figure 1 It is a flowchart of the data classification evaluation method for sewage monitoring based on big data provided by the embodiment of the present application. Detailed Embodiment

[0022] The embodiment of the present application provides a data classification evaluation method for sewage monitoring based on big data, which solves the problem of insufficient accuracy in the classification evaluation and processing of the original data of sewage monitoring based on big data in the prior art. The first classification evaluation and the second classification evaluation are respectively carried out on different data acquisition points according to the early warning results; the second classification adjustment is carried out according to the second comparative analysis result; the third classification adjustment is carried out according to the third comparative analysis result, achieving the effect of improving the accuracy of the classification evaluation and processing of the original data of sewage monitoring.

[0023] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the drawings in the specification and specific embodiments.

[0024] As Figure 1As shown in the figure, it is a flowchart of a data classification and evaluation method for sewage monitoring based on big data provided by an embodiment of the present application. The method includes the following steps: dividing the total sewage monitoring area to obtain different sewage monitoring sub-areas, and obtaining the original data at the data collection points in the sewage monitoring sub-areas from the database; performing a concentration threshold comparison and analysis on the original data, giving an early warning according to the result of the concentration threshold comparison and analysis, and performing a first classification evaluation and a second classification evaluation on different data collection points respectively according to the early warning result; performing a first comparison analysis according to the first classification evaluation result, performing a first classification adjustment according to the result of the first comparison analysis, performing a second comparison analysis according to the second classification evaluation result, and performing a second classification adjustment according to the result of the second comparison analysis; obtaining a comprehensive classification evaluation result according to the first classification evaluation result and the second classification evaluation result, performing a third comparison analysis according to the comprehensive classification evaluation result, and performing a third classification adjustment according to the result of the third comparison analysis.

[0025] In this embodiment, the rule for dividing the total sewage monitoring area is set independently by relevant personnel.

[0026] The original data has been collected and stored in the database, and the original data has a one-to-one or many-to-one relationship with the data collection points in the sewage monitoring sub-areas, that is, at least one original data corresponds to one data collection point in a sewage monitoring sub-area.

[0027] Further, giving an early warning according to the result of the concentration threshold comparison and analysis specifically includes: classifying the original data according to predefined data categories to obtain the original data under different categories; if the original data under different categories is equal to or greater than the corresponding concentration threshold, then record the data collection points in the corresponding sewage monitoring sub-areas as first-level early warning points and notify relevant personnel; if the original data under different categories is less than the corresponding concentration threshold, then record the data collection points in the corresponding sewage monitoring sub-areas as second-level early warning points.

[0028] In this embodiment, the corresponding concentration threshold refers to different concentration thresholds under different categories, and the concentration threshold can be directly extracted from the database.

[0029] The concentration threshold is usually calculated in nanograms per liter (ng / L) and can sensitively capture ultra-trace metabolites in sewage.

[0030] It can detailly count and analyze the detection frequencies and concentrations of various types and their metabolites, so as to provide accurate targeted guidance for anti-drug related personnel.

[0031] Further, first - level classification evaluation and second - level classification evaluation are respectively carried out on different data acquisition points according to the early - warning results, which specifically include: obtaining medical waste data corresponding to the data acquisition points through the medical waste database in the sewage monitoring sub - area, and carrying out the first - level classification evaluation according to the original data and the medical waste data at the data acquisition points; if the data acquisition point is marked as a second - level early - warning point, carrying out the second - level classification evaluation according to the original data at the data acquisition point.

[0032] In this embodiment, the medical waste data refers to the specially marked data for monitoring interference set manually, specifically referring to the component data of the components in medical waste that may be misjudged as its metabolites.

[0033] Further, carrying out the first - level classification evaluation according to the original data and the medical waste data at the data acquisition point specifically includes: sequentially numbering the data acquisition points, sequentially numbering the types of medical waste in the medical waste data, and sequentially numbering the monitoring time periods corresponding to the original data; directly extracting the medical concentration data, concentration detection threshold, sewage standard temperature, and sewage standard pH value from the medical waste database in the sewage monitoring sub - area; collecting and extracting the sewage temperatures of different types of medical waste in different monitoring time periods at different data acquisition points from the temperature acquisition device; collecting and extracting the sewage pH values of different types of medical waste in different monitoring time periods at different data acquisition points from the pH value acquisition device; comprehensively analyzing the medical concentration data, concentration detection threshold, sewage standard temperature, sewage standard pH value, the sewage temperatures of different types of medical waste in different monitoring time periods at different data acquisition points, and the sewage pH values of different types of medical waste in different monitoring time periods at different data acquisition points to obtain the concentration correction value.

[0034] In this embodiment, the components in medical waste that may be misjudged as the drug and its metabolites, such as: analgesics, etc., are used for clinical analgesia, but their metabolites may be similar to some metabolites; sedatives, such as benzodiazepines (such as diazepam), barbiturates, etc., are used to treat anxiety, insomnia, etc., and their metabolites may be misjudged; antidepressants, such as selective serotonin reuptake inhibitors (SSRIs), their metabolites may be similar to some metabolites; chemical disinfectants, chlorine-containing disinfectants, such as sodium hypochlorite, may produce ketamine-like metabolites; drug metabolites, antibiotic metabolites: the metabolites of some antibiotics may be misjudged as metabolites; antiepileptic drug metabolites, such as the metabolites of phenytoin sodium; anesthetics, such as ether, halothane, etc., are used for clinical anesthesia, but their metabolites may be misjudged; solvents, such as acetone, ethyl acetate, etc., are used for laboratory or medical device cleaning, and their metabolites may be similar to some metabolites; radioactive drugs, radioactive drugs used for diagnosis or treatment, and their metabolites may be misjudged. The medical waste data includes the above types of data, which are collected by the intelligent monitoring device at the same data collection points of the original data.

[0035] Number the data collection points in sequence. Represents the number of the data collection point. , Represents the total number of the data collection point numbers.

[0036] Number the types of medical waste in the medical waste data in sequence. Represents the number of the type of medical waste. , Represents the total number of the numbers of the types of medical waste. It should be noted that only the types of medical waste that affect the detection results are numbered.

[0037] Number the monitoring time periods corresponding to the original data in sequence. Represents the number of the monitoring time period. , Represents the total number of the numbers of the monitoring time periods.

[0038] Represents the concentration correction value of the nth data collection point, which is used to quantify the relative negative degree value of the concentration monitoring affected by interference. The larger the concentration correction value, the higher the actual concentration relative to the collected detection value; the smaller the concentration correction value, the lower the actual concentration relative to the collected detection value.

[0039] ; Represents the natural constant.

[0040] Represents the The th monitoring time period of the th type of medical waste corresponding to the concentration value in the original data. The original data actually contains the medical concentration data corresponding to the type of medical waste. For example, the collected concentration of pethidine may be 190 ng / L. Among them, 190 ng / L is the original data, and 140 ng / L is obtained from the collection of medical waste. Therefore, 140 ng / L is the medical waste data.

[0041] Therefore, the smaller the difference between the concentration value and the medical concentration data, the greater the interference of the medical concentration data on the concentration value, making the actual concentration higher relative to the collected and detected value, so the concentration correction value is larger.

[0042] Indicates the th monitoring time period of the th type of medical waste corresponding to the th data collection point. The medical waste data includes the medical concentration data, which is directly extracted from the medical waste database in the sewage monitoring sub-region.

[0043] Indicates the th concentration detection threshold for the type of medical waste. The concentration detection threshold is directly extracted from the medical waste database in the sewage monitoring sub-region, which means that when the concentration is lower than this threshold, the corresponding collection equipment will not be able to detect effective data.

[0044] Indicates the th monitoring time period of the th type of medical waste corresponding to the th data collection point. The sewage temperature is collected and extracted from the temperature collection equipment.

[0045] Indicates the th sewage standard temperature for the type of medical waste. The sewage standard temperature is directly extracted from the medical waste database in the sewage monitoring sub-region.

[0046] Through experiments of controlling the temperature in the laboratory, the solubility and degradation rate at different temperatures are measured, and a temperature-concentration relationship curve is established. The median solubility point in the temperature-concentration relationship curve is used as the sewage standard temperature. Generally, the lower the temperature, the slower the corresponding degradation rate, and the relatively higher the collected concentration. Generally, the higher the temperature, the faster the corresponding degradation rate. If the temperature is high, but an excessive concentration is still detected, it indicates that the actual situation is more serious.

[0047] Indicates the th data collection point, the th monitoring time period, and the th sewage pH value of the medical waste type. The sewage pH value is collected and extracted from the pH value collection device.

[0048] Indicates the th sewage standard pH value of the medical waste type. The sewage standard pH value is directly extracted from the medical waste database in the sewage monitoring sub-region.

[0049] Some are unstable under alkaline conditions and may decompose, resulting in a low detected concentration. An increase in pH value may cause the molecule to transform into other forms, affecting the detection. Some are unstable under acidic conditions and may decompose, resulting in a low detected concentration. A decrease in pH value may cause the molecule to transform into other forms, affecting the detection. Through laboratory experiments on controlling the pH value, the stability and transformation under different pH values are determined, and a pH-concentration relationship curve is established. The sewage standard pH value is determined based on the minimum solubility point in the pH-concentration relationship curve.

[0050] The larger the ratio of the sewage temperature to the sewage standard temperature, the faster the relative degradation rate of the concentration. Therefore, the higher the actual concentration relative to the collected and detected value, the greater the relative difference between the sewage pH value and the sewage standard pH value, and the faster the relative degradation rate of the concentration. So the higher the actual concentration relative to the collected and detected value.

[0051] Indicates the sewage temperature correction factor, which is directly extracted from the medical waste database in the sewage monitoring sub-region.

[0052] Indicates the sewage pH value correction factor, which is directly extracted from the medical waste database in the sewage monitoring sub-region.

[0053] The sewage temperature correction factor and the sewage pH value correction factor respectively represent the relative influence correction levels of the ambient temperature corresponding to the data collection time on the sewage temperature and the sewage pH value.

[0054] The sewage temperature correction factor and the sewage pH value correction factor are directly obtained from the AC trial transmission instrument database through a preset mapping relationship. For example, a mapping set of the data collection time of the real-time sewage temperature and sewage pH value and their corresponding sewage temperature correction factors and sewage pH value correction factors is constructed. The data collection time of the real-time sewage temperature and sewage pH value is input into the mapping set to obtain the corresponding sewage temperature correction factor and sewage pH value correction factor, where the mapping relationship is a one-to-one or many-to-one relationship. Different seasonal variations or day-night temperature differences may affect the sewage temperature and pH value at the same time, thereby affecting the detection. Therefore, the data collection time of the sewage temperature and sewage pH value needs to be taken into consideration.

[0055] Furthermore, a first classification adjustment is performed according to the first comparative analysis result, which specifically includes: if the concentration correction value of a first-level warning point is equal to or greater than the concentration correction threshold, the corresponding first-level warning point is recorded as a special-level warning point and relevant personnel are notified to conduct on-site detection of the sewage monitoring sub-region where the special-level warning point is located; if the concentration correction value of a first-level warning point is less than the concentration correction threshold, the corresponding first-level warning point is still recorded as a first-level warning point and the original data collection frequency of the corresponding first-level warning point is increased; if the concentration correction value of a second-level warning point is equal to or greater than the concentration correction threshold, the corresponding second-level warning point is recorded as a first-level warning point, the original data collection frequency of the corresponding second-level warning point is increased, and the original data of the corresponding second-level warning point is processed by a local anomaly factor detection algorithm; if the concentration correction value of a second-level warning point is less than the concentration correction threshold, the corresponding second-level warning point is recorded as a third-level warning point, and the original data of the corresponding third-level warning point is subjected to similar data cleaning through a fuzzy matching algorithm.

[0056] In this embodiment, if the concentration correction value of a first-level warning point is equal to or greater than the concentration correction threshold, it indicates that the actual concentration is even more than the detected one, and the detected concentration has exceeded the threshold, which means the actual poisoning situation is more serious. Therefore, the corresponding first-level warning point needs to be recorded as a special-level warning point and relevant personnel are notified to conduct on-site detection of the sewage monitoring sub-region where the special-level warning point is located.

[0057] If the concentration correction value of a first-level warning point is less than the concentration correction threshold, it indicates that the actual concentration may exceed the threshold. Due to the influence of the collection frequency and other factors, the concentration during collection does not exceed the threshold. Therefore, the corresponding first-level warning point still needs to be recorded as a first-level warning point and the original data collection frequency of the corresponding first-level warning point is increased.

[0058] If the concentration correction value of the secondary warning point is equal to or greater than the concentration correction threshold, the corresponding secondary warning point will be recorded as a primary warning point, and the original data collection frequency of the corresponding secondary warning point will be increased. Also, the original data of the corresponding secondary warning point will be processed using the local outlier factor detection algorithm. It should be noted that at this time, it is necessary to exclude whether the misjudgment of the warning point is caused by data anomalies, and the local outlier factor detection algorithm needs to be used for processing. The local outlier factor is an algorithm for anomaly detection that identifies outliers by considering the local density of data points. The specific example steps are as follows: Data cleaning, removing obvious incorrect data, such as negative values or values outside the reasonable range; Selecting features related to concentration, such as time, location, detection method, etc.; Standardization, standardizing the data so that different features have the same scale; Manually selecting a suitable neighborhood size k, which is usually determined based on experience or through cross-validation; Manually selecting a distance metric, such as Euclidean distance or Manhattan distance; For each original data point, calculating its distance to the kth neighbor, called the k-distance; For each original data point, calculating its reachable distance to all neighbors; Based on the reachable distance, calculating the local reachable density of each data point; For each original data point, calculating its LOF value, which is the average of the ratio of its local reachable density to the local reachable density of its neighbors; The higher the LOF value, the more likely the point is to be an outlier relative to its neighbors; According to the distribution of the LOF values, setting an anomaly threshold, usually selecting points with an LOF value greater than 1 as potential outliers; Identifying points with LOF values exceeding the threshold as outliers; Manually determining the identified outliers to determine whether they are real anomalies or data errors.

[0059] If the concentration correction value of the secondary warning point is less than the concentration correction threshold, the corresponding secondary warning point will be recorded as a tertiary warning point, and the original data of the corresponding tertiary warning point will be subjected to similar data cleaning using the fuzzy matching algorithm. Using the fuzzy matching algorithm (such as the Levenshtein distance) to detect and clean similar data, the example steps are as follows: Converting the text to a unified format, such as converting all to lowercase; Levenshtein distance, calculating the minimum number of edit operations (insertion, deletion, replacement) required to convert one string into another between two strings; Applying the selected fuzzy matching algorithm to each pair of text records and calculating the similarity score between them; Score interpretation, the lower the score, the more similar the two strings are; Setting a similarity score threshold according to actual needs, and considering text pairs with scores lower than this threshold as similar; Screening similar records, marking text pairs with similarity scores lower than the threshold as similar records; For the identified similar records, choosing to merge or delete according to needs.

[0060] It should be noted that in terms of the severity of the drug situation, the special warning points are the highest, followed by the primary warning points, then the secondary warning points, and the tertiary warning points are the lowest.

[0061] Further, performing first classification adjustment according to the first comparative analysis result further includes: counting the number of first-level warning points, the number of second-level warning points, and the number of third-level points in the sewage monitoring sub-region; if the number of first-level warning points in the sewage monitoring sub-region is equal to or greater than the first-level warning point number threshold, then mark all points in the corresponding sewage monitoring sub-region as special-level warning points; if the number of first-level warning points in the sewage monitoring sub-region is less than the first-level warning point number threshold, then do not adjust; if the number of second-level warning points in the sewage monitoring sub-region is equal to or greater than the second-level warning point number threshold, then mark all points in the corresponding sewage monitoring sub-region as first-level warning points; if the number of second-level warning points in the sewage monitoring sub-region is less than the second-level warning point number threshold, then do not adjust; if the number of third-level warning points in the sewage monitoring sub-region is equal to or greater than the third-level warning point number threshold, then mark all points in the corresponding sewage monitoring sub-region as second-level warning points; if the number of third-level warning points in the sewage monitoring sub-region is less than the third-level warning point number threshold, then do not adjust.

[0062] In this embodiment, in the actual detection situation, hospital sewage must be strictly treated before being discharged. For example, after hospital sewage is treated and discharged into the urban sewage treatment plant, the residual chlorine index of the treated water is 2 to 8 mg / L; if hospital sewage is not discharged into the urban sewage treatment plant after treatment, the residual chlorine index of the treated water is 3 to 10 mg / L; the residual cyanide index of the treated sewage in infectious disease hospitals is 6.5 to 10 mg / L, and it needs to undergo two chlorine addition disinfection treatments. However, there is no specific corresponding treatment for medical waste that is easily confused with data, which is still likely to cause misjudgment. Due to the crisscrossing of urban underground pipelines, if serious toxic conditions are detected at multiple collection points, then the actual toxic condition must be more serious, and the classification evaluation level of the points needs to be increased.

[0063] Further, performing second classification evaluation according to the original data at the data collection points specifically includes: directly extracting the sewage flow standard value, rainfall standard value, and concentration standard value from the medical waste database in the sewage monitoring sub-region; collecting and extracting the concentrations of different types at different monitoring time periods at different second-level warning points from the intelligent flow statistics device; collecting and extracting the sewage flow and rainfall at different monitoring time periods at different second-level warning points from the intelligent flow statistics device; comprehensively analyzing the sewage flow standard value, rainfall standard value, concentration standard value, the concentrations of different types at different monitoring time periods at different second-level warning points, and the sewage flow and rainfall at different monitoring time periods at different second-level warning points to obtain the concentration mutation degree value.

[0064] In this embodiment, number the second-level warning points in sequence, indicating the number of the second-level warning point, , represents the total number of serial numbers of the secondary warning points.

[0065] Number the types in the original data of the medical waste data in sequence, represents the serial number of the type, , represents the total number of serial numbers of the types.

[0066] Number the corresponding monitoring time periods of the original data in sequence, represents the serial number of the monitoring time period, , represents the total number of serial numbers of the monitoring time periods.

[0067] represents the concentration mutation degree value of the th secondary warning point, which is used to quantify the relative level value of the concentration monitoring mutation degree. The larger the concentration mutation degree value, the higher the mutation of the actual concentration within a period of time; the smaller the concentration mutation degree value, the lower the mutation of the actual concentration within a period of time.

[0068] When the sewage flow rate increases, it is diluted, resulting in a lower detected concentration. At the same time, the increase in flow rate may enhance the sewage stirring and affect the distribution, making the detected concentration even lower. When the sewage flow rate decreases, the concentration is relatively high and the detected concentration is high. The decrease in flow rate may cause precipitation and affect the accuracy of the detection result.

[0069] Similarly, rainfall will dilute the sewage, resulting in a lower detected concentration. At the same time, rainfall may bring in surface pollutants and interfere with the detection. When the rainfall decreases, the concentration is relatively high and the detected concentration is high.

[0070] An increase in the flow velocity will dilute it, resulting in a lower detected concentration. An increase in the flow velocity may also enhance the sewage stirring and affect the distribution.

[0071] ; represents the th secondary warning point, the th type, and the maximum concentration of the th monitoring time period.

[0072] represents the th secondary warning point, the th type, and the minimum concentration of the th monitoring time period.

[0073] represents the The concentration standard values of various types are directly extracted from the medical waste database in the sewage monitoring sub-region.

[0074] Indicates the th second-level warning point of the th type of the maximum sewage flow during the

[0075] Indicates the th second-level warning point of the th type of the minimum sewage flow during the

[0076] Indicates the sewage flow standard value of the th second-level warning point, and the concentration standard value is directly extracted from the medical waste database in the sewage monitoring sub-region.

[0077] Indicates the correction factor of sewage flow velocity for sewage flow, with a value range of (0, 1). The correction factor of sewage flow velocity for sewage flow is directly extracted from the medical waste database in the sewage monitoring sub-region. For example, a real-time mapping set of sewage flow velocity and its corresponding correction factor of sewage flow velocity for sewage flow is constructed. The real-time sewage flow velocity is input into the mapping set to obtain the corresponding mapping set of the correction factor of sewage flow velocity for sewage flow, where the mapping relationship is one-to-one or many-to-one. As the flow velocity and flow rate increase, it is diluted and the detected concentration is on the low side. An increase in flow velocity may enhance sewage stirring and affect the distribution, resulting in a low detected concentration. When the flow velocity and flow rate decrease, the concentration is relatively high and the detected concentration is on the high side. A slow flow velocity may cause precipitation and affect the detection. At the same time, at the same flow rate, different flow velocities result in different degrees of dilution.

[0078] Indicates the th second-level warning point of the maximum rainfall during the

[0079] Indicates the th second-level warning point of the rainfall standard value during the th monitoring period, which is directly extracted from the medical waste database in the sewage monitoring sub-region. The rainfall standard value is used to indicate that rainfall outside a certain value will not affect the statistical accuracy. For example, it corresponds to five percent of the historical average sewage flow at the second-level warning point. An increase in rainfall dilutes the sewage and reduces the concentration. At the same time, rainfall may bring in surface pollutants and interfere with the detection. Install a rain gauge to record the rainfall and conduct correlation analysis with the concentration data.

[0080] Further, perform a second classification adjustment based on the second comparative analysis result, which specifically includes: if the concentration mutation degree value of a secondary warning point is equal to or greater than the concentration mutation degree threshold, mark the corresponding secondary warning point as a primary warning point, increase the original data collection frequency of the corresponding primary warning point, and notify relevant personnel to perform data cleaning on the original data of the corresponding primary warning point; if the concentration mutation degree value of a secondary warning point is less than the concentration mutation degree threshold, still mark the corresponding secondary warning point as a secondary warning point.

[0081] In this embodiment, if the concentration mutation degree value of a secondary warning point is equal to or greater than the concentration mutation degree threshold, it indicates that the data fluctuation amount at this point is relatively large. It may be a monitoring point with a large flow rate in the underground sewage pipeline or a monitoring point located near many hospitals and chemical plants. It is necessary to increase the original data collection frequency of the corresponding primary warning point and notify relevant personnel to perform data cleaning on the original data of the corresponding primary warning point.

[0082] Further, obtain a comprehensive classification evaluation result based on the first classification evaluation result and the second classification evaluation result, which specifically includes: comprehensively analyze the concentration correction value and the concentration mutation degree value of the secondary warning point to obtain a comprehensive concentration data evaluation value of the secondary warning point, which is used to quantitatively evaluate the comprehensive level of the concentration data monitoring quality; number the secondary warning points in sequence, denotes the number of the secondary warning point, , denotes the total number of the secondary warning points; the specific comprehensive analysis formula is as follows: ; denotes the comprehensive concentration data evaluation value of the th data collection point, which is used to quantitatively evaluate the comprehensive concentration data evaluation value affected by concentration monitoring interference; denotes the concentration correction value of the th data collection point, which is used to quantitatively evaluate the relative negative degree value affected by concentration monitoring interference; denotes the concentration mutation degree value of the th secondary warning point, which is used to quantitatively evaluate the relative level value of the concentration monitoring mutation degree; denotes the sewage dissolved oxygen content influence correction factor of the th secondary warning point, which is used to quantitatively evaluate the relative level value of the concentration monitoring mutation degree, denotes the weight factor of the concentration correction value of the th secondary warning point for the comprehensive concentration data evaluation value, denotes the weight factor of the concentration mutation degree value of the th secondary warning point for the comprehensive concentration data evaluation value.

[0083] In this embodiment, the data acquisition points include secondary warning points. By evaluating the above parameters of the secondary warning points once again, the concentration correction value of the th data acquisition point can be obtained.

[0084] Denote the influence correction factor of the dissolved oxygen content of the sewage at the th secondary warning point. The value range is (1, 2). The influence correction factor of the dissolved oxygen content of the sewage is directly extracted from the medical waste database in the sewage monitoring sub-region. For example, a mapping set of the real-time dissolved oxygen content of the sewage and its corresponding influence correction factor of the dissolved oxygen content of the sewage is constructed. The real-time dissolved oxygen content of the sewage is input into the mapping set to obtain the corresponding mapping set of the influence correction factor of the dissolved oxygen content of the sewage. The mapping relationship therein is a one-to-one or many-to-one relationship. Dissolved oxygen can participate in redox reactions, which may change the chemical forms of some substances, thus affecting the accuracy of their detection. Dissolved oxygen is an important substance for the growth and metabolism of microorganisms. High dissolved oxygen levels may promote the degradation of by microorganisms and reduce its concentration; while low dissolved oxygen levels may lead to weakened microbial activity and reduced degradation.

[0085] The weight factor of the concentration correction value for the comprehensive evaluation value of the concentration data and the weight factor of the concentration mutation degree value for the comprehensive evaluation value of the concentration data are directly extracted from the medical waste database in the sewage monitoring sub-region. Different parameters have different degrees of influence on the monitoring quality of the concentration data. For example, both the concentration correction value and the concentration mutation degree value are important indicators, but their influences on the final evaluation result may be different. The weight factor can reflect this difference in importance. For example, the concentration mutation degree value may be very sensitive to the concentration change in the short term, while the concentration correction value may be more sensitive to the long-term change. The weight factor can reflect this difference in sensitivity. This makes the degree of influence on the comprehensive evaluation value of the concentration data more accurate. For example, a mapping set of the time duration of the monitoring time period and its corresponding weight factor of the concentration correction value for the comprehensive evaluation value of the concentration data and the weight factor of the concentration mutation degree value for the comprehensive evaluation value of the concentration data is constructed. The time duration of the monitoring time period is input into the mapping set to obtain the corresponding mapping set of the weight factor of the concentration correction value for the comprehensive evaluation value of the concentration data and the weight factor of the concentration mutation degree value for the comprehensive evaluation value of the concentration data. The mapping relationship therein is a one-to-one or many-to-one relationship.

[0086] Further, perform third classification adjustment according to the third comparative analysis result, specifically including: if the comprehensive evaluation value of the concentration data of the secondary warning point is less than the comprehensive evaluation threshold of the concentration data, clean the original data of the corresponding secondary warning point through the standard score algorithm; if the comprehensive evaluation value of the concentration data of the secondary warning point is equal to or greater than the comprehensive evaluation threshold of the concentration data, mark the corresponding secondary warning point as a primary warning point and increase the original data collection frequency of the corresponding secondary warning point; generate an electronically mapped report with classification labels for all data collection points, including special warning points, primary warning points, secondary warning points, and tertiary warning points, in the sewage monitoring sub-region through the report generation model.

[0087] In this embodiment, if the comprehensive evaluation value of the concentration data of the secondary warning point is less than the comprehensive evaluation threshold of the concentration data, clean the original data of the corresponding secondary warning point through the standard score algorithm. Z-score is a statistic that measures the deviation of a single data point from the mean of a data set. In data analysis and statistics, Z-score is often used to standardize data, identify outliers, and compare values between different data sets. Explanation of Z-score: A Z-score of 0 indicates that the data point is exactly equal to the mean. A Z-score greater than 0 indicates that the data point is above the mean. A Z-score less than 0 indicates that the data point is below the mean. The larger the absolute value of the Z-score, the farther the data point is from the mean and it may be an outlier. Outlier detection: Usually, data points with an absolute value of the Z-score greater than 3 are considered likely to be outliers (according to the 3σ principle).

[0088] The report generation model uses advanced LLM (Large Language Model) technology to automatically generate a detailed and scientific sewage monitoring analysis report based on the output results of the data analysis model and the warning model. This model not only improves the efficiency of report generation but also ensures the accuracy and depth of the report content, providing strong decision-making support for anti-drug work.

[0089] The electronically mapped report refers to an electronic map that marks special warning points, primary warning points, secondary warning points, and tertiary warning points in the corresponding sewage monitoring sub-region.

[0090] Application of the LLM: The report generation model uses the LLM as the core generation engine. The LLM has powerful natural language processing and deep learning capabilities, enabling it to understand and generate complex text content. By training the LLM model to be familiar with the professional terms, analysis methods, and report formats in the sewage monitoring field, it can accurately convert the output results of the data analysis model and the warning model into a professional monitoring analysis report.

[0091] The report is detailed: The generated sewage monitoring and analysis report is detailed, comprehensively covering all analysis dimensions of the data analysis model and the early warning model. The report first outlines the background, purpose, and importance of the monitoring project, and then details the data analysis results, including key indicators such as the detection status of target substances, abuse situations, and major abuse trends. At the same time, the report will also highlight the findings of the early warning model, such as new abuse warnings, drug use abuse warnings, cross-abuse warnings, etc., providing emergency response suggestions for relevant departments.

[0092] Data visualization and chart display: To improve the readability and intuitiveness of the report, the report generation model also utilizes the text generation ability of the large language model (LLM) and combines it with data visualization technology to generate rich charts and graphs. These charts include, but are not limited to, bar charts, line charts, pie charts, maps, etc., which can intuitively display key information such as the geographical distribution of abuse, temporal change trends, and correlations between different factors. Through chart display, the current situation and trends of abuse can be more intuitively understood, providing strong support for formulating targeted anti-drug strategies.

[0093] Conclusions and suggestions: When generating the report, the report generation model will also automatically extract the conclusions and suggestions section based on the output results of the data analysis model and the early warning model. The conclusions section will summarize the main findings of the monitoring project, including key information such as the main types of abuse, severity, and geographical distribution.

[0094] Customized report generation: To meet the needs and preferences of different users, the report generation model also provides a customized report generation function. Users can select the content, format, chart types, etc. of the report according to their own needs, and can even customize key information such as the title and abstract of the report. Through the customized report generation function, users can obtain the required monitoring and analysis reports more flexibly, improving work efficiency and decision-making effectiveness.

[0095] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block of the flowchart illustrations and / or block diagrams, and combinations of flows and / or blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart flow or flows and / or block or blocks. Figure 1 in a flow or flows and / or block or blocks Figure 1 or blocks.

[0097] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions specified in the flowchart flow or flows and / or block or blocks. Figure 1 in a flow or flows and / or block or blocks Figure 1 or blocks.

[0098] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and / or block or blocks. Figure 1 in a flow or flows and / or block or blocks Figure 1 or blocks.

[0099] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0100] It is apparent that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A data classification and evaluation method for sewage monitoring based on big data, characterized in that, It includes the following steps: Divide the total sewage monitoring area to obtain different sewage monitoring sub-areas, and obtain the original data at the data collection points in the sewage monitoring sub-areas from the database; Conduct a concentration threshold comparison analysis on the original data, issue a warning according to the result of the concentration threshold comparison analysis, and conduct a first classification evaluation and a second classification evaluation on different data collection points respectively according to the warning result; Conduct a first comparison analysis according to the result of the first classification evaluation, conduct a first classification adjustment according to the result of the first comparison analysis, conduct a second comparison analysis according to the result of the second classification evaluation, and conduct a second classification adjustment according to the result of the second comparison analysis; Obtain a comprehensive classification evaluation result according to the result of the first classification evaluation and the result of the second classification evaluation, conduct a third comparison analysis according to the comprehensive classification evaluation result, and conduct a third classification adjustment according to the result of the third comparison analysis.

2. The data classification and evaluation method for sewage monitoring based on big data according to claim 1, wherein The warning issued according to the result of the concentration threshold comparison analysis specifically includes: Classify the original data according to predefined data categories to obtain the original data under different categories; If the original data under different categories is equal to or greater than the corresponding concentration threshold, mark the data collection points in the corresponding sewage monitoring sub-areas as first-level warning points and notify relevant personnel; If the original data under different categories is less than the corresponding concentration threshold, mark the data collection points in the corresponding sewage monitoring sub-areas as second-level warning points.

3. The data classification and evaluation method for sewage monitoring based on big data according to claim 1, characterized in that, The first classification evaluation and the second classification evaluation conducted on different data collection points respectively according to the warning result specifically include: Obtain the medical waste data at the corresponding data collection points through the medical waste database of the sewage monitoring sub-area, and conduct a first classification evaluation according to the original data and the medical waste data at the data collection points; If the data collection point is marked as a second-level warning point, conduct a second classification evaluation according to the original data at the data collection point.

4. The data classification and evaluation method for sewage monitoring based on big data according to claim 3, characterized in that, The first classification evaluation conducted according to the original data and the medical waste data at the data collection points specifically includes: Number the data collection points in sequence, number the types of medical waste in the medical waste data in sequence, and number the monitoring time periods corresponding to the original data in sequence; Directly extract the medical concentration data, concentration detection threshold, sewage standard temperature, and sewage standard pH value from the medical waste database of the sewage monitoring sub-area; Collect and extract the sewage temperatures of different types of medical waste at different data collection points in different monitoring time periods from the temperature collection device; Collect and extract the sewage pH values of different types of medical waste at different data collection points in different monitoring time periods from the pH value collection device; Comprehensively analyze the medical concentration data, concentration detection threshold, sewage standard temperature, sewage standard pH value, sewage temperatures of different types of medical waste at different data collection points in different monitoring time periods, and sewage pH values of different types of medical waste at different data collection points in different monitoring time periods to obtain a concentration correction value.

5. The data classification and evaluation method for sewage monitoring based on big data according to claim 1, characterized in that The first classification adjustment conducted according to the result of the first comparison analysis specifically includes: If the concentration correction value of a first-level warning point is equal to or greater than the concentration correction threshold, the corresponding first-level warning point is recorded as a special-level warning point, and relevant personnel are notified to conduct on-site detection of the sewage monitoring sub-region where the special-level warning point is located; If the concentration correction value of a first-level warning point is less than the concentration correction threshold, the corresponding first-level warning point is still recorded as a first-level warning point, and the original data collection frequency of the corresponding first-level warning point is increased; If the concentration correction value of a second-level warning point is equal to or greater than the concentration correction threshold, the corresponding second-level warning point is recorded as a first-level warning point, the original data collection frequency of the corresponding second-level warning point is increased, and the original data of the corresponding second-level warning point is processed by a local anomaly factor detection algorithm; If the concentration correction value of a second-level warning point is less than the concentration correction threshold, the corresponding second-level warning point is recorded as a third-level warning point, and the original data of the corresponding third-level warning point is subjected to similar data cleaning through a fuzzy matching algorithm.

6. The data classification and evaluation method for sewage monitoring based on big data according to claim 1, characterized in that The first classification adjustment according to the first comparative analysis result further includes: Count the number of first-level warning points, the number of second-level warning points, and the number of third-level points in the sewage monitoring sub-region; If the number of first-level warning points in the sewage monitoring sub-region is equal to or greater than the first-level warning point number threshold, all points in the corresponding sewage monitoring sub-region are recorded as special-level warning points; If the number of first-level warning points in the sewage monitoring sub-region is less than the first-level warning point number threshold, no adjustment is made; If the number of second-level warning points in the sewage monitoring sub-region is equal to or greater than the second-level warning point number threshold, all points in the corresponding sewage monitoring sub-region are recorded as first-level warning points; If the number of second-level warning points in the sewage monitoring sub-region is less than the second-level warning point number threshold, no adjustment is made; If the number of third-level warning points in the sewage monitoring sub-region is equal to or greater than the third-level warning point number threshold, all points in the corresponding sewage monitoring sub-region are recorded as second-level warning points; If the number of third-level warning points in the sewage monitoring sub-region is less than the third-level warning point number threshold, no adjustment is made.

7. The data classification and evaluation method for sewage monitoring based on big data according to claim 3, wherein The second classification evaluation according to the original data at the data collection points specifically includes: Directly extract the sewage flow standard value, rainfall standard value, and concentration standard value from the medical waste database of the sewage monitoring sub-region; Collect and extract the concentrations of different types at different monitoring time periods under different second-level warning points from the intelligent flow statistics device; Collect and extract the sewage flow and rainfall at different monitoring time periods under different second-level warning points from the intelligent flow statistics device; Comprehensively analyze the sewage flow standard value, rainfall standard value, concentration standard value, the concentrations of different types at different monitoring time periods under different second-level warning points, and the sewage flow and rainfall at different monitoring time periods under different second-level warning points to obtain the concentration mutation degree value.

8. The data classification and evaluation method for sewage monitoring based on big data according to claim 1, characterized in that The second classification adjustment according to the second comparative analysis result specifically includes: If the concentration mutation degree value of the secondary warning point is equal to or greater than the concentration mutation degree threshold, the corresponding secondary warning point will be recorded as a primary warning point, the original data collection frequency of the corresponding primary warning point will be increased, and relevant personnel will be notified to perform data cleaning on the original data of the corresponding primary warning point; If the concentration mutation degree value of the secondary warning point is less than the concentration mutation degree threshold, the corresponding secondary warning point will still be recorded as a secondary warning point.

9. The data classification and evaluation method for sewage monitoring based on big data according to claim 1, characterized in that The obtaining of the comprehensive classification evaluation result according to the first classification evaluation result and the second classification evaluation result specifically includes: The comprehensive evaluation value of the concentration data of the secondary warning point is obtained through comprehensive analysis of the concentration correction value and the concentration mutation degree value of the secondary warning point, which is used to quantitatively evaluate the comprehensive level of the concentration data monitoring quality; Number the secondary warning points in sequence, indicating the number of the secondary warning point, , indicating the total number of the secondary warning points; The specific comprehensive analysis formula is as follows: ； Indicates the comprehensive evaluation value of the concentration data at the th data acquisition point, which is used to quantify the comprehensive evaluation value of the concentration data affected by interference in concentration monitoring; Indicates the concentration correction value of the nth data acquisition point, which is used to quantify the relative negative degree value of the concentration monitoring affected by interference; Indicates the concentration mutation degree value of the -th secondary warning point, which is used to quantify the relative level value of the concentration monitoring mutation degree; Indicates the sewage dissolved oxygen content impact correction factor of the th secondary early warning point, which is a relative level value used to quantify the degree of mutation in concentration monitoring; Indicates the weight factor of the concentration correction value of the th secondary warning point with respect to the comprehensive evaluation value of the concentration data, Indicates the weight factor of the degree of concentration mutation value of the th secondary warning point with respect to the comprehensive evaluation value of the concentration data.

10. The data classification and evaluation method for sewage monitoring based on big data according to claim 1, characterized in that, The performing of the third classification adjustment according to the third comparison and analysis result specifically includes: If the comprehensive evaluation value of the concentration data of the secondary warning point is less than the concentration data comprehensive evaluation threshold, the original data of the corresponding secondary warning point will be cleaned through the standard score algorithm; If the comprehensive evaluation value of the concentration data of the secondary warning point is equal to or greater than the concentration data comprehensive evaluation threshold, the corresponding secondary warning point will be recorded as a primary warning point and the original data collection frequency of the corresponding secondary warning point will be increased; The special warning points, primary warning points, secondary warning points, and tertiary warning points among all the data collection points in the sewage monitoring sub-region are used to generate an electronically mapped report with classification labels through the report generation model.

Images