Water environment on-line monitoring system

Abstract

The invention relates to the technical field of water quality analysis, in particular to a water environment on-line monitoring system which comprises the steps of obtaining ammonia nitrogen, total phosphorus and pH value direction identifiers and performing aggregation analysis, identifying trend convergence sections, dividing turning segments and extracting trend features, screening cross trend combinations, concluding jump nodes and identifying interference points. And marking direction mismatch and generating a derailment combination. According to the method, the change of key indexes such as ammonia nitrogen, total phosphorus and pH value is continuously extracted, the load change direction and the cooperation trend are identified, the index fluctuation rhythm and jump nodes are further compared, and the pollution trend structure and direction mismatch combination is extracted, so that potential abnormal sections and interference points are effectively revealed; the identification depth and the time sequence analysis capability of the dynamic pollution in the reclaimed water conveying process are enhanced, so that the urban resident water is treated to form the reclaimed water, the water quality is stable and qualified after the reclaimed water is conveyed to an industrial park, and more accurate and structured monitoring support is provided for the water quality compliance of the industrial water.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality analysis, and in particular to an online water environment monitoring system. Background Art

[0002] The field of water quality analysis technology includes the quantitative detection and identification of physical, chemical and biological parameters in water bodies, aiming to evaluate and monitor the environmental quality of water bodies, ensure the stability of the ecosystem and the safety of human water use. The core content of this technical field is to identify and measure the main suspended matter in water through multiple types of sensors, analytical instruments and detection processes. Common detection items include dissolved oxygen, chemical oxygen demand, total phosphorus, total nitrogen, ammonia nitrogen, heavy metal ions, pH value and turbidity. Water quality analysis technology usually combines online sampling, data acquisition, result analysis and remote communication systems to form an integrated monitoring process, and is widely used in real-time environmental monitoring of various types of water bodies such as surface water, groundwater, industrial wastewater and domestic sewage.

[0003] Among them, the water environment online monitoring system refers to a data collection and analysis system for real-time water quality testing of water bodies such as urban recycled water. The system mainly covers water sampling, water sample pretreatment, parameter determination, and transmission and storage of monitoring data. It uses a number of detection methods based on electrochemistry, spectrophotometry, ion-selective electrode method, etc. to complete the quantitative monitoring of indicators such as ammonia nitrogen, total phosphorus, total nitrogen, chemical oxygen demand, dissolved oxygen, etc. in the water body, and uses remote communication to transmit the results to the data center for online analysis and recording. The system is usually independently deployed in conjunction with solar power supply units, self-cleaning structures, data recording devices, etc. to complete the continuous observation and data collection of dynamic environmental parameters of surface water.

[0004] In the existing online monitoring of water environment, although it has the ability to conduct conventional quantitative detection of indicators such as ammonia nitrogen, total phosphorus, and dissolved oxygen in reclaimed water, and can realize real-time upload and centralized storage of data, its analysis and processing methods mainly rely on static threshold judgment, lack in-depth analysis of data trend structure, and it is difficult to effectively identify the potential risk of pollution fluctuations in long-distance transportation. In the process of urban reclaimed water being transported from sewage treatment plants to industrial parks, the water body may cause short-term coordinated fluctuations or mutations in pollutant indicators due to pipeline residues, pressure mutations, or environmental disturbances, but the existing system does not analyze the directional consistency and trend cycle between indicators, and cannot identify the turning point of pollution trend or abnormal change points across indicators. For example, under high temperature conditions in summer, water quality fluctuations in long-distance water transmission sections may intensify, and the single-point judgment mechanism of the existing system is difficult to capture trend changes in a timely manner, which is prone to false alarms or missed reports. The above shortcomings weaken the practical value of the system in dynamic supervision, early warning judgment, and pollution tracking, and reduce the reliability of water quality assurance for industrial users during the reuse of reclaimed water. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an online water environment monitoring system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A water environment online monitoring system comprises: The pollution load calculation module obtains continuous data of ammonia nitrogen, total phosphorus, and pH value, calculates the difference between adjacent time periods and marks the direction, merges the multi-indicator direction identification, and outputs the suspended matter water load convergence section label; The abnormal section division module extracts the index change amplitude according to the suspended matter water load convergence section label, identifies the direction turning point and divides it into sections according to the continuous direction, records the trend intensity and duration, and forms an abnormal pollution section trend structure table; The rate trajectory screening module extracts the direction and rhythm of ammonia nitrogen and chemical oxygen demand based on the abnormal pollution segment trend structure table, and screens cross-trend pollution indicator segments with similar starting points and consistent directions; The jump coincidence judgment module extracts the high-frequency coincidence time points based on the indicator jump nodes in the cross-trend pollution indicator group segment, determines whether they are interference influence points, and generates a multi-indicator common jump node set; The indicator derailment identification module analyzes the directions of total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen at three previous and subsequent time points based on the multi-indicator common jump node set. If more than two indicators are continuously mismatched, it is determined to be a derailment combination and the environmental monitoring results are generated.

[0007] As a further scheme of the present invention, the suspended water load convergence section label includes the difference value of indicators in adjacent time periods, a unified direction identifier, a consistency frequency of change direction, and a time range of the convergence period. The abnormal pollution section trend structure table includes the direction turning point position, the direction fragment sequence, the indicator fluctuation amplitude, and the trend duration. The cross-trend pollution indicator group segment includes the combination relationship between ammonia nitrogen and chemical oxygen demand, the indicator trend direction, the trend cycle proximity, and the starting point position consistency. The multi-indicator common jump node set includes the indicator direction change time point, the jump node overlap frequency, and the cross-indicator jump feature. The derailment combination includes the direction mismatch of total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen, the mismatch time period, and the continuous mismatch indicator group.

[0008] As a further solution of the present invention, the pollution load calculation module includes: The indicator difference calculation submodule calculates the numerical difference of each indicator in adjacent time periods based on the continuous detection records of ammonia nitrogen, total phosphorus and pH value, sorts them in timeline order, obtains the direction identification of the indicator at the differentiated time point, and generates the indicator direction identification sequence; The trend direction classification submodule calls the indicator direction identification sequence, extracts the direction identifications of multiple indicators at the same time point, and classifies all time points with consistent direction identifications by comparing whether the indicator directions are consistent, and accumulates the frequency of time points with consistent directions to generate a consistent direction frequency distribution; The convergence segment identification submodule sets a frequency threshold and performs screening according to the frequency distribution of the consistent direction, extracts the time segment where the frequency of the consistent direction exceeds the threshold continuously, and uses the formula: ; Obtain the convergence coefficient by calculation, determine whether the convergence coefficient exceeds the segment classification boundary value, and obtain the suspended matter water load convergence segment label; in, Representative Suspended water direction indicator for each time period, Represents the direction of ammonia nitrogen. Represents the total phosphorus direction indicator, Represents the pH value direction mark, For the The frequency of directional consistency in the time period, is the number of time periods with consistent direction, is the interval between every two time points, is the coefficient of convergence.

[0009] As a further solution of the present invention, the abnormal section division module includes: The calculation acquisition submodule extracts the original value data sequence of the suspended matter water index within the time period based on the time period defined in the suspended matter water load convergence section label, detects the difference sequence of the original values ​​of the index between adjacent time nodes, calculates the change amplitude of the difference, and obtains the index change amplitude value; The direction identification submodule determines the sign change trend according to the change amplitude value of the indicator, makes continuity judgment on the direction identification sequence, compares the difference between the current direction identification and the direction identification of the adjacent time period, identifies whether there is a positive and negative value conversion phenomenon as a direction turning point, and obtains a direction turning point position point set; The structure construction submodule calls the direction turning point set, re-divides the section of the direction turning point into each direction segment, and calculates the fluctuation range of the suspended matter water index change and the duration of the segment for each direction segment, using the formula: ; The intensity of the fluctuation trend is integrated with the length value to obtain the trend structure table of abnormal pollution sections; in, Indicates the trend strength value of abnormal pollution section, Indicates The fluctuation range of the suspended matter index in each direction segment, Represents the average fluctuation range value of all direction segments, Indicates The duration of the directional segments, represents the total number of directional segments, Representative The duration of the segment, Represents the average fluctuation range value.

[0010] As a further solution of the present invention, the rate trajectory screening module includes: The trend extraction submodule obtains the start and end time information of each segment based on the segment division in the abnormal pollution segment trend structure table, and collects the original concentration value sequence of ammonia nitrogen and chemical oxygen demand in the corresponding time period, and calculates the change amplitude of adjacent values ​​per hour, that is, if the current value minus the previous value is greater than zero, it is marked as positive, otherwise it is negative, and the time period with the same continuous direction mark is divided as a rhythm cycle segment, and the direction sequence and rhythm cycle length set of each indicator in the paragraph are constructed to obtain the indicator trend cycle sequence group; The cycle comparison submodule calls the indicator trend cycle sequence group, extracts the rhythm cycle length values ​​of the three indicators in each paragraph in turn, uses a multi-indicator pairing combination method, performs normalized difference analysis on the cycle value sequences of any two indicators, and introduces cycle sequence disturbance to smooth the cycle difference, using the formula: ; The operation obtains the rhythm cycle convergence value of the indicator pair in the current paragraph. If If the period is less than 0.5 and is close to the threshold, it is marked as a period closeness, and a period closeness pair combination is generated; in, represents the rhythm cycle convergence value, , For the Section , The rhythm cycle value of the indicator, For the The disturbance of the segment index to the period, For indicators The average period under all segments, is the total number of paragraphs; The cross-identification submodule extracts the starting value difference in each segment and calculates the absolute difference according to the indicators in the periodic approaching pair combination. If the first direction identifiers of the two indicators are consistent, they are identified as cross-trend segments, and the corresponding indicator combination and segment number are recorded to obtain the cross-trend contamination indicator group segment.

[0011] As a further solution of the present invention, the jump coincidence determination module includes: The jump node statistics submodule calls all indicator combinations in the cross-trend pollution indicator group segment, extracts the trend direction sequence in the corresponding segment for each group of indicators, marks the jump nodes for the time series, repeats the operation to extract ammonia nitrogen and chemical oxygen demand, and counts the jump node positions of the indicators in the segment to obtain a set of direction jump time points; The overlap frequency summary submodule extracts nodes where two or three indicators change direction at the same time point according to the direction jump time point set, counts the frequency of overlap jump nodes appearing in all paragraphs and sorts them, using the formula: ; Calculate and obtain the transition coincidence measurement value, and determine whether the degree of coincidence exceeds the set coincidence frequency threshold, and obtain the high-frequency coincidence transition node; in, represents the node coincidence metric value, For the The frequency of node overlap in the segment, is the mean frequency of node overlap in all paragraphs, is the number of overlaps of each indicator jump of the node in the segment, is the number of time periods with consistent direction; The interference point determination submodule compares the trend consistency and change amplitude of the three indicators at the corresponding time points according to the high-frequency coincident jump nodes, and determines whether there is a situation where the direction is consistent and the change amplitude is greater than the corresponding indicator jump reference value at the same time point. If the judgment result is that it is met at the same time, the time point is classified as an interference influence point, and a multi-indicator common jump node set is established.

[0012] As a further solution of the present invention, the indicator derailment identification module includes: The indicator trend extraction submodule obtains the original indicator sequence of ammonia nitrogen and chemical oxygen demand in each section in turn based on the concentrated segment start and end records of the multi-indicator common jump node, extracts the sequence of indicators in each section according to the time sequence and calculates the direction identification of adjacent values, judges the positive and negative changes according to the difference, and constructs the direction sequence, counts the time length of the same direction appearing continuously in the section and divides the rhythm cycle sequence, and generates the trend direction sequence and rhythm sequence set of the three indicators; The cycle approach judgment submodule calls the trend direction sequence and rhythm sequence set of the three indicators, and respectively judges the rhythm cycle values ​​of ammonia nitrogen and chemical oxygen demand in each section in pairs, using the formula: ; The difference between the rhythmic cycles of any two indicator combinations in the segment is obtained by calculation, and the difference is compared with the set cycle approach threshold. If it is less than 0.2, it is judged as a cycle approach, and then a cycle approach combination group is obtained; in, Indicates the rhythm cycle difference, and Respectively represent Section , The periodic value of the indicator, Indicates The harmonic coefficient of the periodic disturbance, is the number of time periods with consistent direction; The cross-segment identification submodule determines whether the starting point data difference of the corresponding indicator combination in each segment is less than the set approach value based on the periodic approach combination group, and compares the consistency of the first value of the direction sequence. If both conditions are met, it is marked as a cross-trend indicator segment, and all indicator combinations that meet the conditions are output in correspondence with the paragraph index value to obtain the generated environmental monitoring results.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by continuously extracting changes in key indicators such as ammonia nitrogen, total phosphorus, and pH value, the direction of load change and coordinated trends are identified, the indicator fluctuation rhythm and jump nodes are further compared, and the pollution trend structure and direction mismatch combination are extracted, which effectively reveals potential abnormal sections and interference points, and enhances the recognition depth and time series analysis capabilities of pollution dynamics in the process of reclaimed water transportation. In this way, the water used by urban residents is treated to form reclaimed water, and the water quality is stable and qualified after being transported to industrial parks, providing more accurate and structured monitoring support for the compliance of industrial water quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 It is a flow chart of the pollution load calculation module of the present invention; Figure 3 It is a flow chart of the abnormal section division module of the present invention; Figure 4 It is a flow chart of the rate trajectory screening module of the present invention; Figure 5 It is a flow chart of the jump coincidence determination module of the present invention; Figure 6 This is a flow chart of the indicator derailment identification module of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0017] See also Figure 1 , a water environment online monitoring system comprises: The pollution load calculation module obtains the continuous detection records of ammonia nitrogen, total phosphorus, and pH value, sorts the difference values ​​of each indicator in adjacent time periods according to the timeline, marks the direction, performs merging processing on the direction identification of multiple indicators at the same time point, determines whether the direction of suspended matter water change is consistent, sets the frequency threshold, and classifies the period with high consistency frequency as the load convergence stage, and outputs it as the suspended matter water load convergence section label; The abnormal section division module extracts the change range of the original value of the indicator within the period based on the period defined in the suspended matter water load convergence section label, and compares the continuity of the direction mark to identify whether there is a directional turning point as the basis for section structure division. In the section with a turning point, the section is divided into sections according to the directional fragments, and the intensity and duration of the indicator fluctuation trend are recorded section by section to generate an abnormal pollution section trend structure table; The rate trajectory screening module extracts the trend direction and change rhythm of ammonia nitrogen and chemical oxygen demand according to the segment start and end records in the abnormal pollution section trend structure table, determines whether the trend cycles of the three indicators in each section are close, and identifies whether there are cross-segments with similar starting points and consistent directions, and outputs the indicator combinations and segments that meet the conditions as cross-trend pollution indicator group segments; The jump coincidence judgment module calls the combination appearing in the cross-trend pollution index group segment, counts the time points where the direction changes in the time period, and summarizes the most frequently occurring coincident jump nodes to determine whether they have the simultaneous jump characteristics across indicators. If so, the node is classified as an interference influence point to form a multi-indicator common jump node set; The indicator detrack identification module extracts the change direction of total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen within three time points before and after the time points where multiple indicators jump together, and marks whether there is an inconsistent direction trend between multiple consecutive time points. If there are more than two indicator pairs with continuous direction mismatches, they are classified as a detrack combination and generate environmental monitoring results.

[0018] The labels of the convergence sections of suspended water load include the difference values ​​of indicators in adjacent time periods, unified direction identification, consistency frequency of change direction, and time range of the convergence period. The trend structure table of abnormal pollution sections includes the position of the direction turning point, the sequence of direction fragments, the fluctuation amplitude of indicators, and the duration of the trend. The cross-trend pollution indicator group includes the combination relationship between ammonia nitrogen and chemical oxygen demand, the indicator trend direction, the proximity of the trend cycle, and the consistency of the starting point position. The set of common jump nodes for multiple indicators includes the time point of indicator direction change, the frequency of jump node overlap, and the cross-indicator jump characteristics. The derailment combination includes the direction mismatch of total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen, the mismatch time period, and the continuous mismatch indicator group.

[0019] See also Figure 2 , the pollution load calculation module includes: The indicator difference calculation submodule calculates the numerical difference of each indicator in adjacent time periods based on the continuous detection records of ammonia nitrogen, total phosphorus and pH value, sorts them in timeline order, obtains the direction identification of the indicator at the differentiated time point, and generates the indicator direction identification sequence; First, read the hourly water quality data from January 1 to January 2, 2025 from the set monitoring system. The collection frequency of each type of indicator is set to 1 hour, that is, 24 records per day. Select the fixed monitoring points set in the test river section to obtain the values ​​of the four types of indicators at each time point. For example, at 08:00, the ammonia nitrogen is 1.6 mg / L, the total phosphorus is 0.25 mg / L, and the pH value is 7.10. At 09:00, they are 5.6 mg / L, 1.8 mg / L, 0.30 mg / L, and 7.15, respectively. Then calculate the numerical differences of each indicator between 08:00 and 09:00, and the difference values ​​are 0.4 mg / L, 0.2 mg / L, 0.05 mg / L, and 0. 05, where each difference value represents the numerical change of the indicator in the adjacent time periods. The direction of this group of differences is uniformly marked. If the difference is greater than 0, the direction is recorded as +1, equal to 0 as 0, and less than 0 as -1. In this example, the direction marks of the four indicators are all +1, forming a direction mark array [+1, +1, +1, +1]. The direction array is arranged in chronological order, and the indicator changes in each time period are recorded in turn. The same calculation is continued for the indicator values ​​at subsequent time points such as 10:00. This operation forms a complete time series direction array. The direction information of the four indicators is saved at each time point as the basis for subsequent consistency direction judgment, and finally the indicator direction mark sequence is obtained.

[0020] The trend direction classification submodule calls the indicator direction identification sequence, extracts the direction identification of multiple indicators at the same time point, and classifies all time points with consistent direction identification by comparing whether the indicator directions are consistent. It also accumulates the frequency of time points with consistent directions to generate a consistent direction frequency distribution. Read the direction array of the four indicators at each time node, and traverse the direction identification group at each time point to count the number of identical direction values ​​in the group. For example, at a certain time point, the direction group is [+1, +1, 0, +1], of which the +1 direction accounts for 75%, which meets the direction consistency judgment standard. Set the consistency judgment threshold to be greater than or equal to 3 for consistency. Traverse to determine whether each time point meets the standard. If it does, record it as consistent, otherwise it is inconsistent. For example, the direction group at 10:00 is [+1, +1, +1, +1], which is recorded as consistent, and the direction group at 11:00 is [+1, - 1,0,+1], recorded as inconsistent. This operation can form a set of Boolean value sequences such as [1,1,0,1,0,0,1], which represents the direction consistency state at the corresponding time point. At the same time, a cumulative frequency recording method is constructed. If the direction consistency mark in consecutive time points is 1, the count is accumulated. For example, in the above sequence, if the 1st and 2nd bits are consistent, the continuous frequency is 2. If the 3rd bit is inconsistent, the count is reset to zero and accumulated again from the next consistent point. The recorded frequency is used to judge the strength of the continuous convergence segment. For example, if the frequency reaches 4 in a certain time period, it is considered that the convergence is strong. This operation generates a consistent direction frequency distribution.

[0021] The convergence segment identification submodule sets the frequency threshold and performs screening according to the frequency distribution of the consistent direction, extracts the time segment where the frequency of the consistent direction exceeds the threshold continuously, and uses the formula: ; Obtain the convergence coefficient by calculation, determine whether the convergence coefficient exceeds the segment classification boundary value, and obtain the suspended matter water load convergence segment label; in, Representative Time period direction indicator, Represents the direction of ammonia nitrogen. Represents the total phosphorus direction indicator, Represents the pH value direction mark, For the The frequency of directional consistency in the time period, is the number of time periods with consistent direction, is the interval between every two time points, is the coefficient of convergence; The time segments with higher continuous frequency are extracted. For this purpose, the frequency threshold is set to 3, that is, only when the direction is consistent for at least 3 consecutive times in a certain time period can it be identified as a convergence segment. The continuous consistent time points in a certain time period are set to the 5th to 9th hours. The sum of the directions of the four indicators corresponding to each hour in the segment is read and its absolute value is taken. If the sum of the directions of the five time points is 4, 4, 3, 4, 4 respectively, the corresponding directional consistency frequencies are 4, 4, 3, 4, 4, the time interval Δt is 1 hour, and the total number of time points n=5, then it is substituted into the formula for calculation: ; The first step is to calculate each product: i=1:|1+1+1+1|× =4×2=8; i=2:|1+1+1+1|× =4×2=8; i=3:|1+1+1+0|× ≈3×1.732≈5.196; i=4:|1+1+1+1|× =4×2=8; i=5:|1+1+1+1|× =4×2=8; Step 2: Calculate the total: 8+8+5.196+8+8=37.196; Step 3: Substitute the denominator: 5×1=5; The fourth step is to calculate the convergence coefficient: ; The threshold is set to ≥7.0 is used as the standard for judging the convergence segment. The calculation results =7.4392, which meets the threshold condition. The result shows that the changes in the directions of the four types of suspended water show a consistent trend during this period of time, so it can be identified as a suspended water load convergence section label. The strength of the consistent direction is effectively reflected by taking a weighted sum of the absolute value sum of the suspended water direction and the square root of the consistency frequency, and a normalization index is constructed by combining the number of directions and the time distribution, so as to accurately divide the convergence section and improve the responsiveness and robustness of load trend judgment.

[0022] See also Figure 3 , the abnormal segment division module includes: The calculation acquisition submodule extracts the original value data sequence of the suspended matter water index within the time period based on the time period defined in the suspended matter water load convergence section label, detects the difference sequence of the original values ​​of the index between adjacent time nodes, calculates the change range of the difference, and obtains the index change range value; First, extract the specific time range from the tag. For example, the convergence segment corresponding to a certain tag is from 0:00 on July 1, 2024 to 24:00 on July 5, 2024. The system reads the start and end time and calls the complete record data in the original data source of the suspended matter water index accordingly. In the case of a data recording frequency of once per hour, extract the original index values ​​of a total of 120 time points within the 5 days. Each time point corresponds to a suspended matter concentration value, for example, t 1 =2024-07-01-00:00 The suspended solids concentration is 116.4 mg / L, t 2 =2024-07-01-01:00 is 117.9 mg / L, and so on to form an original concentration sequence. Next, the concentration values ​​of two adjacent time points are subtracted to form a difference sequence. For example, the first group of differences is 117.9-116.4=1.5 mg / L, and the difference between 117.9 and 119.6 is 1.7 mg / L. The whole sequence forms a difference sequence with a length of 119. In order to further identify the fluctuation of the difference sequence, it is necessary to set the change amplitude division threshold. The low amplitude threshold is set The high amplitude threshold is set to 1.0mg / L, which means it is within the range of natural fluctuations or slight changes. The high amplitude threshold is set to 4.0mg / L, which represents a situation of strong fluctuations and drastic changes. The threshold is set based on the statistical results of the variation amplitude of the data in the past three months, of which 90% of the fluctuations are concentrated between 1.0 and 4.0. A value below 1.0mg / L is considered a slight change, and a value above 4.0mg / L is a drastic change. On this basis, all differences are divided and classified according to this interval to obtain the fluctuation intensity level label corresponding to each time point and form a sequence of variation amplitude values.

[0023] The direction identification submodule determines the trend of the sign change according to the change amplitude of the indicator, makes a continuity judgment on the direction identification sequence, compares the difference between the current direction identification and the direction identification of the adjacent time period, identifies whether there is a positive and negative value conversion phenomenon as a direction turning point, and obtains the direction turning point position point set; First, a directional comparison is performed on each pair of adjacent original suspended solids concentration values. If the value at the latter time point is higher than that at the previous time point, it is marked as positive, that is, an upward trend; if it is lower, it is marked as negative, that is, a downward trend; if the two are equal, it is marked as no change. This operation covers the entire sequence to form a direction identification sequence. For example, if there are five values ​​of 116.4, 117.9, 119.6, 119.6, and 118.3 in the sequence, the direction identifications are rising, rising, no change, and falling, respectively. Next, a continuity analysis is performed on the direction identification sequence, and the current position is compared item by item to see if the direction is consistent with the previous item. For example, if the current position is rising and the previous item is also rising, it is considered continuous, otherwise it is considered a direction change point. During the traversal process, if it is detected that the direction changes from rising to falling, from falling to rising, or from having a direction to having no direction, it is recorded as a direction turning point. For example, at t4 =2024-07-01-03:00 If it is detected that the price has changed from rising to no change, it will be recorded as a potential turning point. The fluctuation range before and after the turning point will be judged. If the difference before and after exceeds the set significant change threshold, such as 2.5mg / L, the turning point will be included in the set of valid turning points. This threshold is derived from the 30% maximum change range in actual detection as the benchmark for defining the effectiveness of the turning point. By tracking the consistency of the direction of the three time points before and after the two time points of the turning point, it is ensured that the point is not a short-term fluctuation and is misjudged as a trend reversal point.

[0024] The structure construction submodule calls the direction turning point set, re-divides the section of the direction turning point into each directional segment, and calculates the fluctuation range of the suspended matter water index change and the duration of the segment for each directional segment using the formula: ; The intensity of the fluctuation trend is integrated with the length value to obtain the trend structure table of abnormal pollution sections; in, Indicates the trend strength value of abnormal pollution section, Indicates The fluctuation range of the suspended matter index in each direction segment, Represents the average fluctuation range value of all direction segments, Indicates The duration of the directional segments, represents the total number of directional segments, Representative The duration of the segment, Represents the average fluctuation range value.

[0025] Parameter definition and acquisition method description: : represents the fluctuation range of the suspended solids index (SS) in the i-th direction segment, in mg / L. The daily average value sequence is obtained from the water quality testing standard HJ91.1 of the Ministry of Ecology and Environment of China, and the difference between the maximum and minimum values ​​in each direction segment is calculated as the fluctuation range. The recommended daily collection frequency is 1 time / 2 hours for 7 consecutive days; : represents the average fluctuation interval value of all directional segments, that is, all The arithmetic mean of : represents the duration of the i-th direction segment, expressed as the number of continuous monitoring days in the segment. The segments are divided by the turning points of the water flow direction, for example, the direction mutation point is determined by the change of the flow velocity vector; : Indicates the total number of directional segments. Determined by the number of directional turning points in data analysis.

[0026] Basis for setting and obtaining values: Based on the public standard (HJ91.1-2020) and research survey, the following values ​​are set: Number of direction segments ; The first stage of suspended matter fluctuation value mg / L (data are from the difference between the maximum and minimum daily values ​​within 7 days); Paragraph 2 mg / L; Paragraph 3 mg / L; Average Fluctuation mg / L; The duration of the clips are: sky, sky, sky; Formula step-by-step calculation: (a) Molecular calculation: ; (b) Calculation of the denominator: ; (c) Final value calculation: ; Calculation results explanation: The result of 4.03 indicates that the pollution trend fluctuation intensity in the directional turning zone of this section of water is relatively high. This value combines the fluctuation amplitude and the duration of the segment. The larger the value, the more drastic and persistent the pollution change is. This result shows that although the fluctuation amplitude of the second directional segment is low, it has a significant deviation from the average value and makes a strong contribution in the weight calculation.

[0027] See also Figure 4 , the rate trajectory screening module includes: The trend extraction submodule obtains the start and end time information of each segment based on the segment division in the abnormal pollution segment trend structure table, and collects the original concentration value sequence of ammonia nitrogen and chemical oxygen demand in the corresponding time period, and calculates the change amplitude of adjacent values ​​every hour, that is, if the current value minus the previous value is greater than zero, it is marked as positive, otherwise it is negative, and the time period with the same continuous direction mark is divided as a rhythm cycle segment, and the direction sequence and rhythm cycle length set of each indicator in the paragraph are constructed to obtain the indicator trend cycle sequence group; Extract the start and end time of each section in turn, and obtain the original concentration data of the three indicators of ammonia nitrogen and chemical oxygen demand in the corresponding time period, and record their hourly change trends. For example, the start and end time of a section is from 08:00 to 12:00 on July 5, 2024. In this section, the hourly data of suspended water is [4.2, 4.5, 4.7, 4.3, 4.1] mg / L, ammonia nitrogen is [1.1, 1.2, 1.3, 1.1, 1.0] mg / L, and chemical oxygen demand is [18, 19, 19.5, 19, 18.5] mg / L. First, calculate the adjacent hourly difference for each group of data to obtain the ammonia nitrogen change sequence [+0.1, +0.1, -0.2, - 0.1], chemical oxygen demand change sequence [+1,+0.5,-0.5,-0.5], based on these differences, determine the direction mark of each hour, and construct a direction mark sequence such as [+,+,–,–]; further identify the length of the period in the same direction, if the change is positive for two consecutive hours, then these two hours are a rhythm cycle, and the rhythm cycle sequence of each indicator in the segment is divided accordingly. For example, suspended solids water is divided into two cycles in this segment, namely positive 2 hours and negative 2 hours; similarly, ammonia nitrogen and chemical oxygen demand are also divided into positive and negative rhythm segments, and the number and length of rhythm cycles of each indicator in this segment are recorded as trend information, and finally the indicator trend cycle sequence group is obtained.

[0028] The cycle comparison submodule calls the indicator trend cycle sequence group, extracts the rhythm cycle length values ​​of the three indicators in each paragraph in turn, uses a multi-indicator pairing combination method, performs normalized difference analysis on the cycle value sequences of any two indicators, and introduces cycle sequence disturbance to smooth the cycle difference, using the formula: ; The operation obtains the rhythm cycle convergence value of the indicator pair in the current paragraph. If If the period is less than 0.5 and is close to the threshold, it is marked as a period closeness, and a period closeness pair combination is generated; in, represents the rhythm cycle convergence value, , For the Section , The rhythm cycle value of the indicator, For the The disturbance of the segment index to the period, For indicators The average period under all segments, is the total number of paragraphs; Extract the rhythmic cycle values ​​of each indicator in the paragraph, compare the cycles of three groups of indicators: ammonia nitrogen and chemical oxygen demand, ammonia nitrogen and chemical oxygen demand, and calculate using the rhythmic cycle convergence formula value, and determine whether the cycle is close. ) and ammonia nitrogen ( ), the rhythm cycle length sequence in this segment is , , , ; The periodic disturbance value is assumed to be , , the indicator average period is , total number of paragraphs , put it into the formula: ; Compare the above results with the period approach threshold of 0.5, It is determined that the rhythm cycle of suspended matter water and ammonia nitrogen in this section is close, and it is included in the cycle approaching pair combination.

[0029] The cross-identification submodule extracts the starting value difference in each segment and calculates the absolute difference based on the periodic approach to the indicators in the combination. If the first direction identifiers of the two indicators are consistent, they are identified as cross-trend segments, and the corresponding indicator combination and segment number are recorded to obtain the cross-trend contaminated indicator group segment; For each pair of combinations, extract the starting original values ​​of the two indicators in the paragraph where they are judged to be close, and calculate their difference. For example, for suspended water and ammonia nitrogen, the values ​​at the beginning of the paragraph are 4.2 mg / L and 1.1 mg / L, and the difference is 3.1 mg / L. If the proximity threshold is set to 3, the pair of indicators does not meet the starting proximity condition and is not output; the starting values ​​of chemical oxygen demand and ammonia nitrogen are 18 mg / L and 1.1 mg / L, and the difference is 16.9 mg / L, which does not meet the condition; then compare the first label of the direction sequence, if both directions are "+", the directions are considered to be consistent, and only when the starting value difference is less than the threshold and the direction mark is consistent, it is determined to be a cross-trend paragraph, and the indicator pairs that meet the condition and their corresponding paragraph numbers are recorded and output, and finally the cross-trend pollution indicator group segment is obtained.

[0030] See also Figure 5 , the jump coincidence judgment module includes: The jump node statistics submodule calls all indicator combinations in the cross-trend pollution indicator group segment, extracts the trend direction sequence in the corresponding segment for each group of indicators, marks the jump nodes for the time series, repeats the operation to extract ammonia nitrogen and chemical oxygen demand, and counts the jump node positions of indicators in the segment to obtain the direction jump time point set; First, obtain the trend direction sequence of ammonia nitrogen and chemical oxygen demand in each section. The trend direction is compared according to the index values ​​of two adjacent time points. If the latter value is greater than the previous value, the direction is recorded as positive, otherwise it is recorded as negative. If they are equal, it is considered stable. Taking the section from 08:00 to 08:05 on July 3, 2024 as an example, the values ​​of the three indicators are: suspended water: [1.2, 1.0, 1.4, 1.6, 1.1] mg / L, ammonia nitrogen: [0.8, 0.9, 1.0, 0.9, 1.1] mg / L, chemical oxygen demand: [3.5, 3.4, 3.7, 3.2, 3.6] mg / L, and the corresponding direction sequence is: permanganate is [–, + ,+,–], ammonia nitrogen is [+,+,–,+], and chemical oxygen demand is [–,+,–,+]. Based on this, the direction change position points are extracted, that is, the position points where the value changes from positive to negative or from negative to positive in the sequence, and are marked as jump nodes. For example, the direction change of permanganate occurs at the 1st and 4th time points, ammonia nitrogen at the 3rd time point, and chemical oxygen demand at the 1st, 2nd, and 3rd time points. The corresponding time points of these jump nodes in each paragraph are counted to form a set of all jump nodes in the paragraph. For example, permanganate and chemical oxygen demand jump together at the 1st minute, and ammonia nitrogen and chemical oxygen demand jump together at the 3rd minute. Finally, all paragraphs and combinations are traversed to obtain the direction jump time point set under all indicator combinations.

[0031] The overlap frequency induction submodule extracts the nodes where two or three indicators change direction at the same time point according to the direction jump time point set, counts the frequency of overlap jump nodes appearing in all paragraphs and sorts them, using the formula: ; Calculate and obtain the transition coincidence measurement value, and determine whether the degree of coincidence exceeds the set coincidence frequency threshold, and obtain the high-frequency coincidence transition node; in, represents the node coincidence metric value, For the The frequency of node overlap in the segment, is the mean frequency of node overlap in all paragraphs, is the number of overlaps of each indicator jump of the node in the segment, is the number of time periods with consistent direction; The frequency of time nodes with simultaneous jump behaviors of multiple indicators is counted segment by segment, and the node overlap of each segment is measured differently. The node overlap measurement formula is used for calculation. Taking paragraphs 1 to 3 as an example, the jump nodes in paragraph 1 are [1,3], corresponding to 2 and 2 indicator jumps, with frequencies of 2 and 2, respectively. The jump nodes in paragraph 2 are [2,4], with frequencies of 3 and 2, and paragraph 3 is [1,2], with frequencies of 2 and 3. The frequency values ​​of each segment are set to , , , the corresponding number of coincidence indices is , , , the average values ​​of each frequency are: ; Substituting into the formula: ; The numerator is the absolute value of the difference and weighted sum of the number of indicators, and the denominator is the sum of the total frequency and the square root of the sum of the squares of the overlapped items. , this value is greater than the set threshold of 0.25, so it can be determined that there are high-frequency overlapping jump nodes in this paragraph group.

[0032] The interference point determination submodule compares the trend consistency and change amplitude of the three indicators at the corresponding time points according to the high-frequency coincident jump nodes, and determines whether there is a situation where the direction is consistent and the change amplitude is greater than the corresponding indicator jump reference value at the same time point. If the judgment result satisfies both conditions at the same time, the time point is classified as an interference impact point, and a multi-indicator common jump node set is established; The direction identification and change amplitude of the corresponding multiple indicators at that time point are synchronously compared. If the trend directions of the three indicators at a certain node are consistent and the respective change amplitudes exceed the set jump reference value, the node can be determined as an interference impact point. For example, in the 3rd minute period, permanganate changes from 1.4 to 1.6, with a change amplitude of 0.2 mg / L, ammonia nitrogen changes from 1.0 to 0.9, with an amplitude of 0.1 mg / L, and chemical oxygen demand changes from 3.7 to 3.2, with an amplitude of 0.5 mg / L. The corresponding set indicator jump reference values ​​are: permanganate 0.15 mg / L, ammonia nitrogen 0.08 mg / L, and chemical oxygen demand 0.3 mg / L. The three change amplitudes all exceed the reference values, and the directions all show a downward trend, which meets the judgment criteria. The node is classified as an interference impact point. According to this standard, it is determined whether the overlapping nodes in all paragraphs meet the conditions, and those that meet the conditions are integrated to establish a multi-indicator common jump node set.

[0033] See also Figure 6 , the indicator derailment identification module includes: The indicator trend extraction submodule obtains the original indicator sequence of ammonia nitrogen and chemical oxygen demand in each section based on the concentrated segment start and end records of the common jump nodes of multiple indicators. It extracts the sequence of indicators in each section according to the time sequence and calculates the direction identification of adjacent values. It judges the positive and negative changes according to the difference and constructs the direction sequence. It counts the length of time that the same direction appears continuously in the section and divides the rhythm cycle sequence to generate the trend direction sequence and rhythm sequence set of the three indicators. The original monitoring values ​​of total phosphorus, ammonia nitrogen, chemical oxygen demand and dissolved oxygen at three time points before and after each jump node are extracted in turn. The positive and negative values ​​of each difference are judged by performing difference calculation on the original data of two adjacent time points. If the difference is positive, it is recorded as a positive change, if it is negative, it is recorded as a negative change, and if the difference is zero, it is marked as a stable state. A direction sequence consisting of two direction identifiers is constructed in each indicator, and it is stored as a list structure in the form of indicator classification. During the data call process, the difference processing must ensure that the data source is continuous and not missing, and exclude the influence of abnormal value points on the difference direction judgment. Data is retrieved and processed for each indicator separately, and finally a data set with the indicator as the key value and the direction sequence within the time period as the value is formed. This set is used as the basic participating item in the subsequent trend judgment process. Through data standardization processing, the original unit is converted into the direction identifier form to avoid numerical interference and obtain the indicator direction sequence set.

[0034] The cycle approach judgment submodule calls the trend direction sequence and rhythm sequence set of the three indicators, and makes pairwise combination judgments on the rhythm cycle values ​​of ammonia nitrogen and chemical oxygen demand in each section, using the formula: ; The difference between the rhythmic cycles of any two indicator combinations in the segment is obtained by calculation, and the difference is compared with the set cycle approach threshold. If it is less than 0.2, it is judged as a cycle approach, and then a cycle approach combination group is obtained; in, Indicates the rhythm cycle difference, and Respectively represent Section , The periodic value of the indicator, Indicates The harmonic coefficient of the periodic disturbance, is the number of time periods with consistent direction; Call the indicator data recorded in the indicator direction sequence set, and judge whether the two adjacent directions formed by each group of indicators at three consecutive time points are consistent. If there is a conversion from "positive" to "negative" or "negative" to "positive", it is judged as a direction mismatch. If the direction is continuous and consistent or the direction is zero, it is marked as a matching state. Count the number of mismatches in each group of indicator direction sequences, and use the following innovative formula to determine the trend mismatch ratio: ; Now take the data at a certain time point as an example for calculation. Assume that the direction sequences of total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen are [-, +], [+, +], [-, -], and [+, -] respectively. The direction change range of each indicator is calculated as follows: Total phosphorus Δ 1 ^ξ=0.05, ammonia nitrogen Δ2 ^ξ=0.02, chemical oxygen demand Δ 3 ^ξ=0.03, dissolved oxygen Δ 4 ^ξ=0.04, the corresponding consistency judgment values ​​θ are θ 1 ^η=0 (mismatch), θ 2 ^η=1 (match), θ 3 ^η=1 (match), θ 4 ^η=0 (mismatch), the calculation process is as follows: ; ; ; If the threshold is set to 0.02, the S value is greater than the threshold and is judged as a trend mismatch. The result is marked into the trend mismatch indicator value ratio set, which can be used for direction inconsistency judgment in the subsequent derailment judgment module.

[0035] The cross-segment identification submodule determines whether the starting point data difference of the corresponding indicator combination in each segment is less than the set approach value based on the periodic approach combination group, and compares the consistency of the first value of the direction sequence. If both conditions are met, it is marked as a cross-trend indicator segment, and all indicator combinations that meet the conditions are output in correspondence with the segment index value to obtain the generated environmental monitoring results; By comparing with the set threshold, the combinations with two or more indicator direction mismatches at the same time within three time points are screened, and the combinations that meet the conditions are classified as derailed combinations. The system reads the indicator mismatch ratio records at each jump time point in turn, extracts the time, monitoring point number and indicator name of the nodes that meet the mismatch conditions, and builds a structured derailed combination list. At the same time, a combination number is attached to each record for calling. The list is sorted in ascending time order and is consistent with the original data index to avoid node ownership conflicts or overlaps, complete the binding process of nodes and combination structures, and finally establishes an environmental monitoring result data set. The result set can be returned to the main processing logic flow as an output port to obtain the environmental monitoring results.

[0036] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A water environment online monitoring system, characterized in that: The system comprises: The pollution load calculation module obtains continuous data of ammonia nitrogen, total phosphorus, and pH value, calculates the difference between adjacent time periods and marks the direction, merges the multi-indicator direction identification, and outputs the suspended matter water load convergence section label; The abnormal section division module extracts the index change amplitude according to the suspended matter water load convergence section label, identifies the direction turning point and divides it into sections according to the continuous direction, records the trend intensity and duration, and forms an abnormal pollution section trend structure table; The rate trajectory screening module extracts the direction and rhythm of ammonia nitrogen and chemical oxygen demand based on the abnormal pollution segment trend structure table, and screens cross-trend pollution indicator segments with similar starting points and consistent directions; The jump coincidence judgment module extracts the high-frequency coincidence time points based on the indicator jump nodes in the cross-trend pollution indicator group segment, determines whether they are interference influence points, and generates a multi-indicator common jump node set; The indicator derailment identification module analyzes the directions of total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen at three previous and subsequent time points based on the multi-indicator common jump node set. If more than two indicators are continuously mismatched, it is determined to be a derailment combination and the environmental monitoring results are generated.

2. The water environment online monitoring system according to claim 1, characterized in that: The suspended water load convergence section label includes the difference value of indicators in adjacent time periods, a unified direction identifier, the consistency frequency of the change direction, and the time range of the convergence period. The abnormal pollution section trend structure table includes the direction turning point position, the direction fragment sequence, the indicator fluctuation amplitude, and the trend duration. The cross-trend pollution indicator group segment includes the combination relationship between ammonia nitrogen and chemical oxygen demand, the indicator trend direction, the trend cycle proximity, and the starting point position consistency. The multi-indicator common jump node set includes the indicator direction change time point, the jump node overlap frequency, and the cross-indicator jump feature. The derailment combination includes the direction mismatch of total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen, the mismatch time period, and the continuous mismatch indicator group.

3. The water environment online monitoring system according to claim 2 is characterized in that: The pollution load calculation module includes: The indicator difference calculation submodule calculates the numerical difference of each indicator in adjacent time periods based on the continuous detection records of ammonia nitrogen, total phosphorus and pH value, sorts them in timeline order, obtains the direction identification of the indicator at the differentiated time point, and generates the indicator direction identification sequence; The trend direction classification submodule calls the indicator direction identification sequence, extracts the direction identifications of multiple indicators at the same time point, and classifies all time points with consistent direction identifications by comparing whether the indicator directions are consistent, and accumulates the frequency of time points with consistent directions to generate a consistent direction frequency distribution; The convergence segment identification submodule sets a frequency threshold and performs screening according to the frequency distribution of the consistent direction, extracts the time segment where the frequency of the consistent direction exceeds the threshold continuously, and uses the formula: ； Obtain the convergence coefficient by calculation, determine whether the convergence coefficient exceeds the segment classification boundary value, and obtain the suspended matter water load convergence segment label; in, Representative The direction of the suspended matter water index for each period is indicated. Represents the direction of ammonia nitrogen. Represents the total phosphorus direction indicator, Represents the pH value direction mark, For the The frequency of directional consistency in the time period, is the number of time periods with consistent direction, is the interval between every two time points, is the coefficient of convergence.

4. The water environment online monitoring system according to claim 3 is characterized in that: The abnormal section division module includes: The calculation acquisition submodule extracts the original value data sequence of the suspended matter water index within the time period based on the time period defined in the suspended matter water load convergence section label, detects the difference sequence of the original values ​​of the index between adjacent time nodes, calculates the change amplitude of the difference, and obtains the index change amplitude value; The direction identification submodule determines the sign change trend according to the change amplitude value of the indicator, makes continuity judgment on the direction identification sequence, compares the difference between the current direction identification and the direction identification of the adjacent time period, identifies whether there is a positive and negative value conversion phenomenon as a direction turning point, and obtains a direction turning point position point set; The structure construction submodule calls the direction turning point set, re-divides the section of the direction turning point into each direction segment, and calculates the fluctuation range of the suspended matter water index change and the duration of the segment for each direction segment, using the formula: ； The intensity of the fluctuation trend is integrated with the length value to obtain the trend structure table of abnormal pollution sections; in, Indicates the trend strength value of abnormal pollution section, Indicates The fluctuation range of the suspended matter index in each direction segment, Represents the average fluctuation range value of all direction segments, Indicates The duration of the directional segments, represents the total number of directional segments, Representative The duration of the segment, Represents the average fluctuation range value.

5. The water environment online monitoring system according to claim 4, characterized in that: The rate trajectory screening module includes: The trend extraction submodule obtains the start and end time information of each segment based on the segment division in the abnormal pollution segment trend structure table, and collects the original concentration value sequence of ammonia nitrogen and chemical oxygen demand in the corresponding time period, and calculates the change amplitude of adjacent values ​​per hour, that is, if the current value minus the previous value is greater than zero, it is marked as positive, otherwise it is negative, and the time period with the same continuous direction mark is divided as a rhythm cycle segment, and the direction sequence and rhythm cycle length set of each indicator in the paragraph are constructed to obtain the indicator trend cycle sequence group; The cycle comparison submodule calls the indicator trend cycle sequence group, extracts the rhythm cycle length values ​​of the three indicators in each paragraph in turn, uses a multi-indicator pairing combination method, performs normalized difference analysis on the cycle value sequences of any two indicators, and introduces cycle sequence disturbance to smooth the cycle difference, using the formula: ； The operation obtains the rhythm cycle convergence value of the indicator pair in the current paragraph. If If the period is less than 0.5 and is close to the threshold, it is marked as a period closeness, and a period closeness pair combination is generated; in, represents the rhythm cycle convergence value, , For the Section , The rhythm cycle value of the indicator, For the The disturbance of the segment index to the period, For indicators The average period under all segments, is the total number of paragraphs; The cross-identification submodule extracts the starting value difference in each segment and calculates the absolute difference according to the indicators in the periodic approaching pair combination. If the first direction identifiers of the two indicators are consistent, they are identified as cross-trend segments, and the corresponding indicator combination and segment number are recorded to obtain the cross-trend contamination indicator group segment.

6. The water environment online monitoring system according to claim 5, characterized in that: The jump coincidence determination module comprises: The jump node statistics submodule calls all indicator combinations in the cross-trend pollution indicator group segment, extracts the trend direction sequence in the corresponding segment for each group of indicators, marks the jump nodes for the time series, repeats the operation to extract ammonia nitrogen and chemical oxygen demand, and counts the jump node positions of the indicators in the segment to obtain a set of direction jump time points; The overlap frequency summary submodule extracts nodes where two or three indicators change direction at the same time point according to the direction jump time point set, counts the frequency of overlap jump nodes appearing in all paragraphs and sorts them, using the formula: ； Obtain the jump coincidence measurement value by calculation, and determine whether the coincidence degree exceeds the set coincidence frequency threshold, and obtain the high-frequency coincidence jump node; in, represents the node coincidence metric value, For the The frequency of node overlap in the segment, is the mean frequency of node overlap in all paragraphs, is the number of overlaps of each indicator jump of the node in the segment, is the number of time periods with consistent direction; The interference point determination submodule compares the trend consistency and change amplitude of the three indicators at the corresponding time points according to the high-frequency coincident jump nodes, and determines whether there is a situation where the direction is consistent and the change amplitude is greater than the corresponding indicator jump reference value at the same time point. If the judgment result is that it is met at the same time, the time point is classified as an interference influence point, and a multi-indicator common jump node set is established.

7. The water environment online monitoring system according to claim 6, characterized in that: The indicator derailment identification module comprises: The indicator trend extraction submodule obtains the original indicator sequence of ammonia nitrogen and chemical oxygen demand in each section in turn based on the concentrated segment start and end records of the multi-indicator common jump node, extracts the sequence of indicators in each section according to the time sequence and calculates the direction identification of adjacent values, judges the positive and negative changes according to the difference, and constructs the direction sequence, counts the time length of the same direction appearing continuously in the section and divides the rhythm cycle sequence, and generates the trend direction sequence and rhythm sequence set of the three indicators; The cycle approach judgment submodule calls the trend direction sequence and rhythm sequence set of the three indicators, and respectively judges the rhythm cycle values ​​of ammonia nitrogen and chemical oxygen demand in each section in pairs, using the formula: ； The difference between the rhythmic cycles of any two indicator combinations in the segment is obtained by calculation, and the difference is compared with the set cycle approach threshold. If it is less than 0.2, it is judged as a cycle approach, and then a cycle approach combination group is obtained; in, Indicates the rhythm cycle difference, and Respectively represent Section , The periodic value of the indicator, Indicates The harmonic coefficient of the periodic disturbance, is the number of time periods with consistent direction; The cross-segment identification submodule determines whether the starting point data difference of the corresponding indicator combination in each segment is less than the set approach value based on the periodic approach combination group, and compares the consistency of the first value of the direction sequence. If both conditions are met, it is marked as a cross-trend indicator segment, and all indicator combinations that meet the conditions are output in correspondence with the paragraph index value to generate environmental monitoring results.

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