An environmental online data abnormal clue analysis method and system

Abstract

The present application relates to the technical field of water environment data monitoring, and particularly relates to an environmental online data abnormal clue analysis method and system, the present application receives uploaded environmental online data in a collection period, filters out first abnormal data in the environmental online data, selects a filtering standard of abnormal data, statistically processes and collates the filtered environmental online data, generates a time node-pollutant concentration curve, draws a tangent line at each time node in the curve, determines a node slope, records a node with a node slope greater than a preset node slope as an abnormal node, judges the environmental online data based on the abnormal node, generates a corresponding processing instruction or completes the abnormal clue analysis of the environmental online data based on the judgment result, and re-determines the operation parameters of the corresponding components based on the received processing instruction. The present application effectively realizes fast and accurate analysis of the obtained environmental online data, and effectively improves the use efficiency of the environmental online data.

Description

Technical Field

[0001] This invention relates to the field of water environment data monitoring technology, and in particular to a method and system for analyzing anomaly clues in online environmental data. Background Technology

[0002] Environmental online data refers to the real-time collection, transmission, storage, and analysis of various environmental elements and indicators through various online monitoring devices and systems. Anomaly analysis of environmental online data involves in-depth research and interpretation of data obtained from online environmental monitoring to identify abnormal data caused by monitoring equipment malfunctions, ensuring the accuracy and reliability of the data and providing scientific support for environmental management and decision-making. Anomaly analysis of environmental online data in water bodies is of great significance for the timely and accurate detection of environmental pollution, monitoring of ecological changes, and protection of public health.

[0003] Chinese Patent Publication No. CN118780478A discloses an intelligent water environment data analysis system, comprising: a first acquisition module for acquiring river images and information of rivers in the city; a river analysis module for analyzing the main river area and river branch areas based on the river information and river images; a second acquisition module for periodically acquiring river water quality information and monitoring point locations of river water quality monitoring points in the city; a numbering construction module for constructing branch numbers, monitoring point numbers, and main river monitoring point numbers; a water quality analysis module for analyzing the water quality parameters of monitoring points, main river water quality parameters, and main river fluctuation parameters; a monitoring analysis module for analyzing the water quality status of monitoring points and analyzing abnormal locations; and an output module for outputting abnormal locations. This invention achieves accurate monitoring and analysis of urban river water environment anomalies.

[0004] Therefore, the above-mentioned solution, through an intelligent water environment data analysis system, can accurately analyze anomalies in urban river water environments, identify the locations of anomalies, and output these locations. However, this solution cannot perform rapid and accurate analysis of the acquired online environmental data, thus failing to guarantee the efficiency of the use of online environmental data. Summary of the Invention

[0005] To address this issue, the present invention provides a method and system for analyzing anomaly clues in online environmental data, thereby overcoming the problem in existing technologies that cannot perform rapid and accurate analysis of acquired online environmental data, resulting in low efficiency in the use of online environmental data.

[0006] On one hand, the present invention provides a method for analyzing anomaly clues in online environmental data, comprising:

[0007] Receive uploaded online environmental data within the collection period. The online environmental data includes time points, pollutant concentrations, and flow rates.

[0008] The first-level abnormal data in the online environmental data are marked and then filtered out.

[0009] The numerical range of the online environmental data after screening was selected as the screening criterion for abnormal data.

[0010] The filtered online environmental data are statistically analyzed and organized to generate time-pollutant concentration curves;

[0011] Tangent lines are drawn at each time node in the curve, and the absolute value of the slope of the drawn tangent lines is recorded as the node slope.

[0012] Nodes whose slope is greater than the preset slope are recorded as abnormal nodes.

[0013] The online environmental data is determined based on the abnormal nodes;

[0014] Based on the judgment result, generate the corresponding processing instructions.

[0015] Alternatively, perform anomaly analysis on the online environmental data;

[0016] The determination includes determining whether the online environmental data meets the standard and the reasons why it does not meet the standard;

[0017] The operating parameters of the corresponding component are re-determined based on the received processing instructions;

[0018] The process of determining the online environmental data based on the abnormal nodes includes:

[0019] Count the number of abnormal nodes and record the obtained number as the number of abnormal nodes;

[0020] Calculate the ratio of the number of anomalies to the total number of time points, and record the obtained ratio as the anomaly ratio;

[0021] When the anomaly ratio is less than the first preset anomaly ratio, the environmental online data is determined to meet the standard, and the anomaly clue analysis of the environmental online data is completed.

[0022] When the anomaly ratio is greater than or equal to the first preset anomaly ratio and less than the second preset anomaly ratio, it is determined that the online environmental data does not meet the standard, and based on the anomaly node, it is determined whether the slope of the preset node meets the standard.

[0023] When the anomaly ratio is greater than or equal to the second preset anomaly ratio, it is determined that the online environmental data does not meet the standard, and the reason why the online environmental data does not meet the standard is determined based on the node slope of the anomaly node.

[0024] The process of determining whether the slope of the preset node meets the standard based on the abnormal node includes:

[0025] Determine the degree of anomaly distribution for each of the aforementioned abnormal nodes;

[0026] When the abnormal distribution degree is less than or equal to the preset abnormal distribution degree, it is determined that the preset node slope meets the standard, the environmental online data does not meet the standard, and the reason why the environmental online data does not meet the standard is determined based on the node slope of the abnormal node.

[0027] When the abnormal distribution degree is greater than the preset abnormal distribution degree, it is determined that the preset node slope does not meet the standard, and the preset node slope is corrected based on the abnormal ratio.

[0028] In this process, a single abnormal node is selected and designated as the first abnormal node. The abnormal node with the closest straight-line distance to the first abnormal node is designated as the second abnormal node. The distance between the first abnormal node and the second abnormal node is calculated and recorded as the first distance. The abnormal node with the closest straight-line distance to the second abnormal node is designated as the third abnormal node. The distance between the second abnormal node and the third abnormal node is calculated and recorded as the second distance. The straight-line distances between each abnormal node are obtained sequentially. The average value of each straight-line distance is calculated. The horizontal coordinate range of the time node within the acquisition period of the curve is selected. The horizontal coordinate distance between the maximum value and the minimum value of the horizontal coordinate of the time node within the horizontal coordinate range is calculated. The ratio of the average value of each straight-line distance to the horizontal coordinate distance is calculated and recorded as the abnormal distribution degree.

[0029] Furthermore, the process of correcting the preset node slope based on the abnormal ratio includes:

[0030] Calculate the difference between the anomaly ratio and the first preset anomaly ratio, and record the obtained difference as the anomaly ratio difference;

[0031] The slope of the preset node is increased based on the anomaly ratio difference, and the increase in the slope of the preset node is proportional to the anomaly ratio difference.

[0032] Furthermore, the process of determining the reason why the online environmental data does not meet the standard based on the slope of the abnormal node includes:

[0033] Calculate the variance of the slope of each of the abnormal nodes, and record the obtained variance as the slope variance;

[0034] The reason why the online environmental data does not meet the standard is determined based on the slope variance.

[0035] When the slope variance is less than or equal to the first preset slope variance, it is determined that the reason why the environmental online data does not meet the standard is that the number of uploaded environmental online data needs to be increased, and the collection period of the uploaded environmental online data is corrected based on the slope variance.

[0036] When the slope variance is greater than the first preset slope variance and less than or equal to the second preset slope variance, the reason why the online environmental data does not meet the standard is determined based on the flow velocity.

[0037] When the slope variance is greater than the second preset slope variance, the range of the screening criteria for the abnormal data is corrected based on the node slope of the abnormal node.

[0038] Furthermore, the process of collecting the online environmental data based on the slope variance correction includes:

[0039] Calculate the difference between the first preset slope variance and the slope variance, and record the obtained difference as the slope variance;

[0040] The collection period of the uploaded online environmental data is increased based on the oblique variance, and a corresponding notification is issued. The increase in the collection period of the uploaded online environmental data is proportional to the oblique variance.

[0041] Furthermore, the process of adjusting the preset anomaly ratio based on the increase in the collection cycle of the uploaded online environmental data includes:

[0042] The anomaly ratio is increased by the increase in the collection period of the uploaded online environmental data, and the increase in the anomaly ratio is proportional to the increase in the collection period of the uploaded online environmental data.

[0043] Furthermore, the process of determining the reason why the online environmental data does not meet the standard based on the flow rate includes:

[0044] The actual flow rate is calculated based on the flow velocity, and the calculated flow rate is recorded as the actual flow rate.

[0045] Calculate the ratio of the actual flow rate to the expected flow rate, and record the obtained ratio as the flow rate ratio;

[0046] When the flow ratio is less than or equal to the first preset flow ratio, it is determined that the reason why the environmental online data does not meet the standard is that the amount of uploaded environmental online data needs to be increased, and the collection period of the uploaded environmental online data is corrected based on the slope variance.

[0047] When the flow ratio is greater than the first preset flow ratio and less than or equal to the second preset flow ratio, the range of the filtering criteria for the abnormal data is corrected based on the node slope of the abnormal node.

[0048] When the flow ratio is greater than the second preset flow ratio, a tampering notification is issued, and manual verification is required.

[0049] Furthermore, the process of correcting the interval of the screening criteria for the abnormal data based on the node slope of the abnormal node includes:

[0050] Calculate the average value of the slope of each abnormal node, and record the obtained average value as the abnormal average value;

[0051] The range of the screening criteria for abnormal data is reduced based on the average abnormal value, and the reduction in the range of the screening criteria for abnormal data is inversely proportional to the average abnormal value.

[0052] On the other hand, the present invention also provides an anomaly clue analysis system for online environmental data using the above method, comprising:

[0053] The data collection module is used to receive the uploaded online environmental data within the collection period. The online environmental data includes time points, pollutant concentrations, and flow rates.

[0054] The data cleaning module, which is connected to the data collection module, is used to mark and filter out first-level abnormal data in the online environmental data.

[0055] The statistics module, which is connected to the data cleaning module, is used to select the numerical range of the online environmental data after screening as the screening criterion for abnormal data; the statistics module is also used to perform statistics and sorting on the screened online environmental data to generate time node-pollutant concentration curves.

[0056] The feature extraction module, which is connected to the statistics module, is used to draw tangent lines at each time node in the curve and record the absolute value of the slope of the drawn tangent lines as the node slope; the feature extraction module is also used to record nodes whose node slopes are greater than a preset node slope as abnormal nodes.

[0057] An analysis module, connected to the feature extraction module, is used to determine the online environmental data based on the abnormal nodes; the analysis module is also used to generate corresponding processing instructions based on the determination results, or to complete the analysis of abnormal clues in the online environmental data; wherein, the determination includes determining whether the online environmental data meets the standards and the reasons for not meeting the standards.

[0058] The processing module, which is connected to the statistics module, the feature extraction module and the analysis module respectively, is used to redetermine the operating parameters of the corresponding components based on the received processing instructions.

[0059] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention statistically analyzes and organizes first-level abnormal data, generates time-point-pollutant concentration curves, obtains the node slopes, and judges the online environmental data based on abnormal nodes. This enables timely and accurate judgment of whether the online environmental data meets the standards, effectively achieving rapid and accurate analysis of the acquired online environmental data. Based on the judgment results, corresponding processing instructions are generated, and the operating parameters of the corresponding components are re-determined based on the received processing instructions, effectively ensuring the system's operating efficiency. Furthermore, this invention enables rapid and accurate analysis of the acquired online environmental data, effectively ensuring accurate environmental assessment results based on the online environmental data, and effectively improving the utilization efficiency of online environmental data.

[0060] Furthermore, this invention calculates the anomaly ratio based on the number of abnormal nodes, and determines whether the online environmental data meets the standards based on the anomaly ratio. It can accurately determine whether the online environmental data meets the standards based on the uploaded environmental data, thereby further improving the efficiency of using the online environmental data while enabling rapid and accurate analysis.

[0061] Furthermore, when the anomaly ratio is greater than or equal to a first preset anomaly ratio and less than a second preset anomaly ratio, the present invention determines whether the slope of the preset node meets the standard based on the anomaly distribution degree. This effectively determines whether the anomaly ratio is greater than or equal to the first preset anomaly ratio and less than the second preset anomaly ratio due to the slope of the preset node not meeting the standard. This further enables rapid and accurate analysis of the acquired online environmental data and improves the efficiency of using online environmental data.

[0062] Furthermore, this invention increases the preset node slope based on the anomaly ratio difference, effectively avoiding the situation where the anomaly ratio is greater than or equal to the first preset anomaly ratio but less than the second preset anomaly ratio due to the preset node slope not meeting the standard. This effectively ensures the accuracy of the system's judgment on online environmental data, further guaranteeing the system's operating efficiency. While further realizing the rapid and accurate analysis of the acquired online environmental data, it also further improves the efficiency of using online environmental data.

[0063] Furthermore, this invention determines the reasons why online environmental data does not meet the standards based on slope variance, which can timely and accurately determine whether online environmental data meets the standards, avoiding misjudgment. While further realizing rapid and accurate analysis of the acquired online environmental data, it also further improves the efficiency of using online environmental data.

[0064] Furthermore, this invention increases the acquisition cycle of uploaded online environmental data based on oblique variance and issues corresponding notifications, avoiding the situation where the online environmental data is judged to be non-compliant due to the acquisition cycle not meeting the standard. This further ensures the system's operating efficiency and, while enabling faster and more accurate analysis of the acquired online environmental data, further improves the efficiency of using the online environmental data.

[0065] Furthermore, after the collection cycle of the uploaded online environmental data is increased, the present invention corrects the preset anomaly ratio based on the increase, which effectively ensures the coordination between the quantity of online environmental data and the preset anomaly ratio, further ensuring the operating efficiency of the system. While further realizing the rapid and accurate analysis of the acquired online environmental data, it also further improves the utilization efficiency of the online environmental data.

[0066] Furthermore, when the slope variance is greater than the first preset slope variance and less than or equal to the second preset slope variance, the present invention determines the reason why the environmental online data does not meet the standard based on the flow ratio. Combining the flow velocity and slope variance can more accurately determine the reason why the environmental online data does not meet the standard, avoiding misjudgment. While further realizing the rapid and accurate analysis of the acquired environmental online data, it also further improves the efficiency of using environmental online data.

[0067] Furthermore, this invention reduces the range of the abnormal data screening criteria based on the abnormal average value, effectively ensuring that the range of the abnormal data screening criteria can guarantee the accuracy of the judgment of the uploaded environmental online data, further ensuring the system's operating efficiency. While further realizing the rapid and accurate analysis of the acquired environmental online data, it further improves the utilization efficiency of the environmental online data. Attached Figure Description

[0068] Figure 1 This is a structural block diagram of the environmental online data anomaly clue analysis system according to an embodiment of the present invention;

[0069] Figure 2 This is a flowchart of the method for analyzing abnormal clues in online environmental data according to an embodiment of the present invention;

[0070] Figure 3This is a flowchart illustrating how to determine whether online environmental data meets the standard and whether the slope of a preset node meets the standard, as per an embodiment of the present invention.

[0071] Figure 4 This is a flowchart illustrating the reasons why online environmental data does not meet the standards, as described in this embodiment of the invention. Detailed Implementation

[0072] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0073] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0074] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0075] Please see Figure 1 The diagram shown is a structural block diagram of an online environmental data anomaly clue analysis system according to an embodiment of the present invention. The structure of this embodiment includes a data collection module, a data cleaning module, a statistics module, a feature extraction module, an analysis module, and a processing module; wherein,

[0076] The data collection module is used to receive the uploaded online environmental data within the collection period. The online environmental data includes time points, pollutant concentrations, and flow rates.

[0077] The data cleaning module is connected to the data collection module and is used to mark and filter out first-level abnormal data in the online environmental data.

[0078] Specifically, the first-level abnormal data refers to the environmental online data that is less than or equal to zero and the abnormal environmental online data that is greater than zero. The environmental online data that is less than or equal to zero is marked, and the 95th percentile of the other data in the marked environmental online data that is less than or equal to zero is calculated. The environmental online data that is greater than this percentile is recorded as the abnormal environmental online data that is greater than zero, and the abnormal environmental online data that is greater than zero is marked. The marked environmental online data is recorded as the first-level abnormal data.

[0079] The statistics module is connected to the data cleaning module and is used to select the numerical range of the online environmental data after screening as the screening criterion for abnormal data; the statistics module is also used to perform statistics and sorting on the screened online environmental data to generate time node-pollutant concentration curves.

[0080] The feature extraction module is connected to the statistics module and is used to draw tangent lines at each time node in the curve, and record the absolute value of the slope of the drawn tangent lines as the node slope; the feature extraction module is also used to record nodes whose node slopes are greater than a preset node slope as abnormal nodes.

[0081] The analysis module is connected to the feature extraction module and is used to determine the online environmental data based on the abnormal nodes. The analysis module is also used to generate corresponding processing instructions based on the determination results, or to complete the analysis of abnormal clues in the online environmental data. The determination includes determining whether the online environmental data meets the standards and the reasons why it does not meet the standards.

[0082] The processing module is connected to the statistics module, the feature extraction module, and the analysis module respectively, and is used to redetermine the operating parameters of the corresponding components based on the received processing instructions.

[0083] Please see Figure 2 The diagram shown is a flowchart of an anomaly clue analysis method for online environmental data according to an embodiment of the present invention. The method described in this embodiment includes:

[0084] Receive uploaded online environmental data within the collection period. The online environmental data includes time points, pollutant concentrations, and flow rates.

[0085] The first-level abnormal data in the online environmental data are marked and then filtered out.

[0086] The numerical range of the online environmental data after screening was selected as the screening criterion for abnormal data.

[0087] The filtered online environmental data are statistically analyzed and organized to generate time-pollutant concentration curves;

[0088] Tangent lines are drawn at each time node in the curve, and the absolute value of the slope of the drawn tangent lines is recorded as the node slope.

[0089] Nodes whose slope is greater than the preset slope K are recorded as abnormal nodes, wherein, in this embodiment of the invention, the preset slope K = 0.32;

[0090] The online environmental data is determined based on the abnormal nodes;

[0091] Based on the judgment result, generate the corresponding processing instructions.

[0092] Alternatively, perform anomaly analysis on the online environmental data;

[0093] The determination includes determining whether the online environmental data meets the standard and the reasons why it does not meet the standard;

[0094] The operating parameters of the corresponding component are re-determined based on the received processing instructions.

[0095] Please see Figure 3 The diagram shows a flowchart illustrating how an embodiment of the present invention determines whether online environmental data meets standards and whether the slope of a preset node meets standards. The process of determining the online environmental data based on the abnormal nodes in this embodiment includes:

[0096] Count the number of abnormal nodes and record the obtained number as the number of abnormal nodes;

[0097] Calculate the ratio of the number of anomalies to the total number of time points, and record the obtained ratio as the anomaly ratio;

[0098] The anomaly ratio is compared with a preset anomaly ratio.

[0099] When the anomaly ratio is less than the first preset anomaly ratio R1, the environmental online data is determined to meet the standard, and the anomaly clue analysis of the environmental online data is completed. In this embodiment, the first preset anomaly ratio R1 = 1.1%.

[0100] When the anomaly ratio is greater than or equal to the first preset anomaly ratio R1 and less than the second preset anomaly ratio R2, it is determined that the online environmental data does not meet the standard, and the slope of the preset node is determined based on the anomaly node to meet the standard. In this embodiment, the second preset anomaly ratio R2 = 20%.

[0101] When the anomaly ratio is greater than or equal to the second preset anomaly ratio R2, it is determined that the online environmental data does not meet the standard, and the reason why the online environmental data does not meet the standard is determined based on the node slope of the anomaly node.

[0102] Please continue reading. Figure 3 As shown, the process of determining whether the slope of the preset node meets the standard based on the abnormal node in this embodiment of the invention includes:

[0103] The abnormality distribution degree of each abnormal node is determined. A single abnormal node is selected and denoted as the first abnormal node. The abnormal node with the closest straight-line distance to the first abnormal node is denoted as the second abnormal node. The distance between the first abnormal node and the second abnormal node is calculated and recorded as the first distance. The abnormal node with the closest straight-line distance to the second abnormal node is denoted as the third abnormal node. The distance between the second abnormal node and the third abnormal node is calculated and recorded as the second distance. The straight-line distances between each abnormal node are obtained in sequence. The average value of each straight-line distance is calculated. The horizontal coordinate range of the time node of the curve within the collection period is selected. The horizontal coordinate distance between the maximum value and the minimum value of the horizontal coordinate of the time node within the horizontal coordinate range is calculated. The ratio of the average value of each straight-line distance to the horizontal coordinate distance is calculated and recorded as the abnormality distribution degree.

[0104] Based on the abnormal distribution degree, determine whether the slope of the preset node meets the standard;

[0105] When the abnormal distribution degree is less than or equal to the preset abnormal distribution degree D, it is determined that the preset node slope meets the standard, the environmental online data does not meet the standard, and the reason why the environmental online data does not meet the standard is determined based on the node slope of the abnormal node. In this embodiment, the preset abnormal distribution degree D = 18%.

[0106] When the abnormal distribution degree is greater than the preset abnormal distribution degree D, it is determined that the preset node slope does not meet the standard, and the preset node slope is corrected based on the abnormal ratio.

[0107] Please continue reading. Figure 3 As shown, the process of correcting the preset node slope based on the abnormal ratio in this embodiment of the invention includes:

[0108] Calculate the difference between the anomaly ratio and the first preset anomaly ratio, and record the obtained difference as the anomaly ratio difference;

[0109] The preset node slope is increased based on the anomaly ratio difference;

[0110] When the anomaly ratio difference is greater than the second preset anomaly ratio difference △A2, the preset node slope is increased to 1.23 times the initial preset node slope, wherein, in this embodiment, the second preset anomaly ratio difference △A2 = 13.1%;

[0111] When the anomaly ratio difference is less than or equal to the second preset anomaly ratio difference △A2 and greater than the first preset anomaly ratio difference A1, the preset node slope is increased to 1.17 times the initial preset node slope, wherein, in this embodiment, the first preset anomaly ratio difference △A1 = 7.9%;

[0112] When the anomaly ratio difference is less than or equal to the first preset anomaly ratio difference △A1, the preset node slope is increased to 1.09 times the initial preset node slope.

[0113] Please see Figure 4 The diagram shown illustrates a flowchart illustrating the process of determining why online environmental data does not meet standards according to an embodiment of the present invention. The process of determining the reason why online environmental data does not meet standards based on the slope of the abnormal node in this embodiment includes:

[0114] Calculate the variance of the slope of each of the abnormal nodes, and record the obtained variance as the slope variance;

[0115] The reason why the online environmental data does not meet the standard is determined based on the slope variance.

[0116] When the slope variance is less than or equal to the first preset slope variance C1, it is determined that the reason why the environmental online data does not meet the standard is that the number of uploaded environmental online data needs to be increased, and the collection period of the uploaded environmental online data is corrected based on the slope variance. In this embodiment, the first preset slope variance C1 = 0.21.

[0117] When the slope variance is greater than the first preset slope variance C1 and less than or equal to the second preset slope variance C2, the reason why the online environmental data does not meet the standard is determined based on the flow velocity. In this embodiment, the second preset angle variance C2 = 0.58.

[0118] When the slope variance is greater than the second preset slope variance C2, the range of the screening criteria for the abnormal data is corrected based on the node slope of the abnormal node.

[0119] Please continue reading. Figure 4 As shown, the process of collecting the online environmental data based on the slope variance correction in this embodiment of the invention includes:

[0120] Calculate the difference between the first preset slope variance and the slope variance, and record the obtained difference as the slope variance;

[0121] The collection cycle of the uploaded online environmental data is increased based on the oblique variance, and a corresponding notification is issued;

[0122] When the oblique deviation is greater than the second preset oblique deviation △S2, the collection period of the uploaded environmental online data is increased to 1.38 times the collection period of the initially uploaded environmental online data, and a corresponding notification is issued. In this embodiment, the second preset oblique deviation △S2 = 0.15.

[0123] When the oblique deviation is less than or equal to the second preset oblique deviation △S2 and greater than the first preset oblique deviation △S1, the collection period of the uploaded environmental online data is increased to 1.26 times the collection period of the initially uploaded environmental online data, and a corresponding notification is issued. In this embodiment, the first preset slope difference △S1 = 0.08.

[0124] When the oblique deviation is less than or equal to the first preset oblique deviation ΔS1, the collection period of the uploaded environmental online data is increased to 1.11 times the collection period of the initially uploaded environmental online data, and a corresponding notification is issued.

[0125] Please continue reading. Figure 4 As shown, the process of correcting the preset anomaly ratio based on the increase in the collection period of the uploaded online environmental data in this embodiment of the invention includes:

[0126] The anomaly ratio is increased by the increase in the collection period of the uploaded online environmental data;

[0127] When the collection period of the uploaded environmental online data is increased to 1.38 times the initial collection period of the uploaded environmental online data, the preset anomaly ratio is increased to 1.12 times the initial preset anomaly ratio.

[0128] When the collection period of the uploaded environmental online data is increased to 1.26 times the initial collection period of the uploaded environmental online data, the preset anomaly ratio is increased to 1.09 times the initial preset anomaly ratio.

[0129] When the collection period of the uploaded environmental online data is increased to 1.11 times the initial collection period of the uploaded environmental online data, the preset anomaly ratio is increased to 1.05 times the initial preset anomaly ratio.

[0130] Please continue reading. Figure 4 As shown, the process of determining the reason why the online environmental data does not meet the standard based on the flow rate in this embodiment of the invention includes:

[0131] The actual flow rate is calculated based on the flow velocity, and the calculated flow rate is recorded as the actual flow rate.

[0132] Calculate the ratio of the actual flow rate to the expected flow rate, and record the obtained ratio as the flow rate ratio;

[0133] Determine the reason why the online environmental data does not meet the standard based on the aforementioned flow ratio;

[0134] When the flow ratio is less than or equal to the first preset flow ratio Q1, it is determined that the reason why the environmental online data does not meet the standard is that the number of uploaded environmental online data needs to be increased, and the number of uploaded environmental online data is corrected based on the slope variance. In this embodiment, the first preset flow ratio Q1 = 0.92.

[0135] When the flow ratio is greater than the first preset flow ratio Q1 and less than or equal to the second preset flow ratio Q2, the range of the filtering criteria for the abnormal data is corrected based on the node slope of the abnormal node, wherein, in this embodiment, the second preset flow ratio Q2 = 1;

[0136] When the flow ratio is greater than the second preset flow ratio Q2, a tampering notification is issued, and manual verification is required.

[0137] Specifically, when calculating the actual flow rate based on the flow velocity, the actual flow rate is the product of the flow velocity and the cross-sectional area of ​​the water during the flow process.

[0138] Please continue reading. Figure 4 As shown, the process of correcting the interval of the screening criteria for abnormal data based on the node slope of the abnormal node in this embodiment of the invention includes:

[0139] Calculate the average value of the slope of each abnormal node, and record the obtained average value as the abnormal average value;

[0140] The range of the screening criteria for the abnormal data is reduced based on the average abnormal value;

[0141] When the average abnormal value is greater than the second preset average abnormal value V2, the range of the screening criteria is reduced to 0.97 times the range of the initial screening criteria, wherein, in this embodiment, the second preset average abnormal value V2 = 0.81;

[0142] When the average abnormal value is less than or equal to the second preset average abnormal value V2 and greater than the first preset average abnormal value V1, the range of the screening criteria is reduced to 0.94 times the range of the initial screening criteria, wherein, in this embodiment, the first preset average abnormal value V1 = 0.53;

[0143] When the average abnormal value is less than or equal to the first preset average abnormal value V1, the range of the screening criteria is reduced to 0.91 times the range of the initial screening criteria.

[0144] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0145] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An environmental online data abnormal clue analysis method, characterized in that, The method comprises the following steps: receiving uploaded online environmental data in a collection period, the online environmental data comprising time nodes, pollutant concentrations and flow rates; labeling and screening first-level abnormal data in the online environmental data; selecting a numerical range of the screened online environmental data as a screening standard for abnormal data; statistically analyzing and collating the screened online environmental data to generate a time node-pollutant concentration curve; drawing a tangent line at each time node in the curve, and recording the absolute value of the slope of the drawn tangent line as a node slope; labeling a node as an abnormal node if the node slope is greater than a preset node slope; judging the online environmental data based on the abnormal nodes; generating a corresponding processing instruction based on the judgment result, or, completing abnormal clue analysis of the online environmental data; wherein the judgment comprises judging whether the online environmental data meets the standard and the reason why the online environmental data does not meet the standard; re-determining the operating parameters of the corresponding components based on the received processing instruction; wherein the process of judging the online environmental data based on the abnormal nodes comprises: counting the number of abnormal nodes, and recording the obtained number as an abnormal number; calculating the ratio of the abnormal number to the total number of time nodes, and recording the obtained ratio as an abnormal ratio; when the abnormal ratio is less than a first preset abnormal ratio, judging that the online environmental data meets the standard, and completing abnormal clue analysis of the online environmental data; when the abnormal ratio is greater than or equal to the first preset abnormal ratio and less than a second preset abnormal ratio, judging that the online environmental data does not meet the standard, and judging whether the preset node slope meets the standard based on the abnormal nodes; when the abnormal ratio is greater than or equal to the second preset abnormal ratio, judging that the online environmental data does not meet the standard, and judging the reason why the online environmental data does not meet the standard based on the node slope of the abnormal nodes; wherein the process of judging whether the preset node slope meets the standard based on the abnormal nodes comprises: determining the abnormal distribution degree of each abnormal node; when the abnormal distribution degree is less than or equal to a preset abnormal distribution degree, judging that the preset node slope meets the standard, judging that the online environmental data does not meet the standard, and judging the reason why the online environmental data does not meet the standard based on the node slope of the abnormal nodes; when the abnormal distribution degree is greater than the preset abnormal distribution degree, judging that the preset node slope does not meet the standard, and correcting the preset node slope based on the abnormal ratio; The abnormal distribution degree is calculated by the following steps: selecting a single abnormal node as a first abnormal node, obtaining a second abnormal node which is closest to the first abnormal node in a straight line distance, calculating a distance between the first abnormal node and the second abnormal node, and taking the calculated distance as a first distance; obtaining a third abnormal node which is closest to the second abnormal node in a straight line distance, calculating a distance between the second abnormal node and the third abnormal node, and taking the calculated distance as a second distance; sequentially obtaining straight line distances between each pair of abnormal nodes, calculating an average value of the straight line distances, selecting a horizontal coordinate range of the time node of the curve in the collection period, calculating a horizontal coordinate distance between a maximum value of the horizontal coordinate of the time node and a minimum value of the horizontal coordinate of the time node in the horizontal coordinate range, calculating a ratio of the average value of the straight line distances to the horizontal coordinate distance, and taking the calculated ratio as the abnormal distribution degree.

2. The method of claim 1, wherein, The process of correcting the preset node slope based on the abnormal ratio includes: calculating a difference between the abnormal ratio and the first preset abnormal ratio, and taking the calculated difference as an abnormal ratio difference; increasing the preset node slope based on the abnormal ratio difference, and the increasing amplitude of the preset node slope is proportional to the abnormal ratio difference. 3.The environmental online data anomaly clue analysis method according to claim 1, characterized in that, The process of determining the reason why the environmental online data does not meet the standard based on the node slope of the abnormal node includes: calculating a variance of the node slope of each abnormal node, and taking the calculated variance as a slope variance; determining the reason why the environmental online data does not meet the standard based on the slope variance; when the slope variance is less than or equal to a first preset slope variance, determining that the reason why the environmental online data does not meet the standard is that the number of uploaded environmental online data needs to be increased, and correcting the collection period of the uploaded environmental online data based on the slope variance; when the slope variance is greater than the first preset slope variance and less than or equal to a second preset slope variance, determining the reason why the environmental online data does not meet the standard based on the flow rate; when the slope variance is greater than the second preset slope variance, correcting the interval of the screening standard of the abnormal data based on the node slope of the abnormal node.

4. The method of claim 3, wherein, The process of correcting the collection period of the uploaded environmental online data based on the slope variance includes: calculating a difference between the first preset slope variance and the slope variance, and taking the calculated difference as a slope difference; increasing the collection period of the uploaded environmental online data based on the slope difference and issuing a corresponding notification, and the increasing amplitude of the collection period of the uploaded environmental online data is proportional to the slope difference.

5. The method of claim 4, wherein, The process of correcting the preset abnormal ratio based on the increasing amplitude of the collection period of the uploaded environmental online data after the increasing of the collection period of the uploaded environmental online data is completed includes: increasing the abnormal ratio based on the increasing amplitude of the collection period of the uploaded environmental online data, and the increasing amplitude of the abnormal ratio is proportional to the increasing amplitude of the collection period of the uploaded environmental online data.

6. The method of claim 3, wherein, The process of determining the reason why the environmental online data does not meet the standard based on the flow rate includes: calculating an actual flow rate according to the flow rate, and taking the calculated flow rate as an actual flow rate; calculating a ratio of the actual flow rate and the expected flow rate, and recording the obtained ratio as a flow rate ratio; when the flow rate ratio is less than or equal to a first preset flow rate ratio, determining that the reason why the online environmental data does not meet the standard is that the number of uploaded online environmental data needs to be increased, and correcting the collection period of the uploaded online environmental data based on the slope variance; when the flow rate ratio is greater than the first preset flow rate ratio and less than or equal to a second preset flow rate ratio, correcting the interval of the screening standard of the abnormal data based on the node slope of the abnormal node; when the flow rate ratio is greater than the second preset flow rate ratio, issuing a tampering notification, and manual checking needs to be performed.

7. The method of claim 3, wherein the abnormal clue analysis of the online data of the environment is performed by using a machine learning algorithm. The process of correcting the interval of the screening standard of the abnormal data based on the node slope of the abnormal node includes: calculating the average value of the node slope of each abnormal node, and recording the obtained average value as an abnormal average value; based on the abnormal average value, reducing the interval of the screening standard of the abnormal data, and the reduction amplitude of the interval of the screening standard of the abnormal data is inversely proportional to the abnormal average value.

8. An environmental online data anomaly clue analysis system using the method of any one of claims 1-7, characterized in that, comprises: a data collection module configured to receive uploaded online environmental data in a collection period, the online environmental data including time nodes, pollutant concentrations, and flow rates; a data cleaning module connected to the data collection module, configured to mark first abnormal data in the online environmental data and perform screening processing; a statistical module connected to the data cleaning module, configured to select a numerical range of the online environmental data after the screening processing as a screening standard of abnormal data; the statistical module is further configured to statistically analyze and arrange the online environmental data after the screening processing, and generate a time node-pollutant concentration curve; a feature extraction module connected to the statistical module, configured to draw a tangent line at each time node in the curve, and record the absolute value of the slope of the drawn tangent line as a node slope; the feature extraction module is further configured to record a node as an abnormal node when the node slope is greater than a preset node slope; an analysis module connected to the feature extraction module, configured to determine the online environmental data based on the abnormal node; the analysis module is further configured to generate a corresponding processing instruction based on the determination result, or complete the analysis of abnormal clues of the online environmental data; wherein the determination includes determining whether the online environmental data meets the standard and the reason why the online environmental data does not meet the standard; a processing module connected to the statistical module, the feature extraction module, and the analysis module, respectively, configured to determine the operating parameters of the corresponding components based on the received processing instruction.

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