WO2020010701A1 - Procédé et système de surveillance d'une anomalie de polluant, dispositif informatique et support de stockage - Google Patents

Procédé et système de surveillance d'une anomalie de polluant, dispositif informatique et support de stockage Download PDF

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WO2020010701A1
WO2020010701A1 PCT/CN2018/106682 CN2018106682W WO2020010701A1 WO 2020010701 A1 WO2020010701 A1 WO 2020010701A1 CN 2018106682 W CN2018106682 W CN 2018106682W WO 2020010701 A1 WO2020010701 A1 WO 2020010701A1
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data
real
pollutant
index
forest model
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PCT/CN2018/106682
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金戈
徐亮
肖京
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services

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  • the present application relates to the technical field of environmental pollution data processing, and in particular, to a method, a system, a computer device, and a storage medium for monitoring anomalies of pollutants.
  • a pollution source is a source of pollutants that causes environmental pollution. It usually refers to a place, equipment, device, or human body that emits harmful substances to the environment or has a harmful effect on the environment. Any substance or energy that enters the environmental system at an inappropriate concentration, quantity, speed, form, and path and causes pollution or damage to the environment is collectively referred to as a pollutant.
  • a pollutant In some links in industrial production, such as production equipment or production sites used in raw material production, processing, combustion, heating and cooling, and finishing of finished products, they can become sources of industrial pollution.
  • there are generally two methods for monitoring the emissions of pollution sources The first is supervisory monitoring, which periodically checks whether the content of harmful substances in the exhaust gas emitted by pollution sources meets national regulations.
  • the second is research-based monitoring, which monitors the types, emissions, and discharge laws of pollutants emitted by pollution sources, which helps to identify the main sources of air pollution, discuss the development trend of air pollution, formulate pollution control measures, and improve ambient air quality.
  • the threshold of the single pollution source is set, and compared with the detection data of the single pollution source, it is found that the pollutant discharge enterprises exceed the standard.
  • the types of pollution sources are complicated, the setting and checking of single thresholds are cumbersome, and the setting of thresholds cannot prevent the occurrence of excessive standards.
  • a method for monitoring anomalies of pollutants includes the following steps:
  • An index threshold is set for each index item of each of the pollutants, and each of the data sets is filtered according to the index threshold, and data that does not exceed the index threshold is set as a feature item, and the feature is set. Items are stored in non-exceeded data sets;
  • a pollutant abnormality monitoring system includes the following units:
  • the acquiring data unit is configured to acquire historical environmental monitoring data of a pollution source monitoring point of each enterprise from a preset environmental monitoring data system, and set the historical environmental monitoring data to one data of one pollutant per enterprise Set for storage;
  • the screening unit is configured to set an index threshold for each index item of each of the pollutants, filter each of the data sets according to the index threshold value, and filter out data that does not exceed the index threshold value as a feature item , Storing the feature items in a non-exceeding data set;
  • a training unit configured to train an isolated forest model by using the feature terms in the non-exceeded data set, and establish the corresponding isolated forest model for each non-exceeded data set;
  • the abnormal point summary unit is configured to obtain real-time monitoring data of a pollutant in an enterprise in units of hours from the environmental monitoring data system, and input the real-time monitoring data into the isolated forest corresponding to the pollutant In the model, it is determined whether the path length from the root node to the leaf node of the isolated forest model is an abnormal point through the real-time monitoring data, and the abnormal points are summarized.
  • a computer device includes a memory and a processor.
  • the memory stores computer-readable instructions.
  • the processor causes the processor to perform the following steps:
  • An index threshold is set for each index item of each of the pollutants, and each of the data sets is filtered according to the index threshold, and data that does not exceed the index threshold is set as a feature item, and the feature is set. Items are stored in non-exceeded data sets;
  • a storage medium storing computer-readable instructions.
  • the one or more processors execute the following steps:
  • An index threshold is set for each index item of each of the pollutants, and each of the data sets is filtered according to the index threshold, and data that does not exceed the index threshold is set as a feature item, and the feature is set. Items are stored in non-exceeded data sets;
  • the above-mentioned pollutant abnormality monitoring method, device, computer equipment and storage medium include obtaining historical environmental monitoring data of each enterprise's pollution source monitoring point from a preset environmental monitoring data system, and converting historical environmental monitoring data to Each pollutant is set as a data set for storage; each indicator of each pollutant is set with an index threshold, each data set is filtered according to the index threshold, and the data that does not exceed the index threshold is set as a feature.
  • Store the feature items in the non-exceeding data set use the feature items in the non-exceeding data set to train an isolated forest model, and establish a corresponding isolated forest model for each non-exceeding data set; obtain an enterprise-in-one
  • the real-time monitoring data of the pollutants are measured in hours.
  • the real-time monitoring data is input into the isolated forest model corresponding to the pollutants, and the path length of the root node to the leaf node of the isolated forest model is determined by the real-time monitoring data to determine whether it is an abnormal point.
  • the abnormal points are summarized.
  • This application screens historical data and selects non-exceeding pollutant data as feature items, and uses an isolated forest model to monitor abnormal points of the company's sewage.
  • the monitoring of the abnormal point data is simple and fast, and it can predict the pollution source exceeding the standard in advance. And output, to prevent the occurrence of excessive standards.
  • FIG. 1 is a flowchart of a pollutant abnormality monitoring method in an embodiment of the present application
  • FIG. 2 is a flowchart of step S3 in FIG. 1;
  • step S3 is a structural diagram of a tree constructed in step S3;
  • FIG. 4 is a flowchart of step S4 in FIG. 1;
  • FIG. 5 is a structural diagram of a pollutant abnormality monitoring system in an embodiment of the present application.
  • FIG. 6 is a schematic block diagram of the abnormal point summary unit in FIG. 5.
  • FIG. 1 is a flowchart of a method for monitoring abnormal pollutants in an embodiment of the present application. As shown in FIG. 1, the monitoring method includes the following steps:
  • Step S1 Obtaining data: Obtain historical environmental monitoring data of each enterprise's pollution source monitoring point from a preset environmental monitoring data system, and set historical environmental monitoring data with one pollutant of each enterprise as a data set. storage.
  • the abnormality monitoring of pollutants in this step is mainly for the pollutants discharged by enterprises. Therefore, the enterprises in this embodiment are the key pollutant discharge units included in the pollution source monitoring center of the Ministry of Ecology and Environment.
  • the preset environmental monitoring data system is provided by the government environmental protection department.
  • the environmental monitoring data system collected historical environmental monitoring data and real-time monitoring data from all pollution source monitoring points of each key sewage unit.
  • the pollution source monitoring points of the enterprise are generally set at the drainage outlet and the exhaust outlet. Therefore, the pollutants of the enterprise include the drainage pollutants based on the drainage outlet monitoring and the exhaust pollutants based on the exhaust outlet monitoring.
  • the data set is classified and stored according to the drainage pollutant data set and the exhaust pollutant data set.
  • Step S2 screening data: setting index thresholds for each index item of each pollutant, filtering each data set according to the index thresholds, filtering out data that does not exceed the index threshold value, setting them as feature items, and storing the feature items in the Out-of-standard data set.
  • a corresponding index threshold is set for each index item of the pollutant.
  • the index items of drainage pollutants include at least one of suspended solids index, chemical oxygen demand index, pH value or ammonia nitrogen index, and corresponding index thresholds are set for the index items of drainage pollutants.
  • the exhaust pollutant index items include at least one of nitrogen oxide index, sulfur dioxide index, soot index, or carbon monoxide index, and corresponding exhaust thresholds are set for the exhaust pollutant index items.
  • the index items of pollutants and the corresponding index thresholds are shown in Table 1 below:
  • each data set is filtered according to a preset index threshold, and the data that does not exceed the threshold is screened out and stored as a feature item in a non-exceeding data set.
  • the feature items are classified with one pollutant per enterprise and stored in the corresponding non-exceeded data set. That is, the feature items selected by the drainage pollutant data set of each enterprise are stored as the drainage non-standard data set, and the feature items selected by the exhaust pollutant data set of each enterprise are stored as the exhaust non-standard data set.
  • it can be used as a sample of a certain pollutant in the enterprise.
  • Step S3 training the model: using the feature terms in the non-exceeding data set to train an isolated forest model, and establishing a corresponding isolated forest model for each non-exceeding data set.
  • the isolated forest model is a fast anomaly detection method with linear time complexity and high accuracy, and is an algorithm that meets the requirements of big data processing.
  • the isolated forest model is suitable for continuous data anomaly detection.
  • the anomaly is defined as “outliers that are easy to be isolated”, which can be understood as the points that are sparsely distributed and far from the densely populated group. In terms of statistics, that is, in the data space, a sparsely distributed area indicates that the probability of data occurring in this area is very low, so the data falling in these areas can be considered abnormal.
  • the isolated forest model is based on the above principles and builds a binary tree from the samples: the input training data set A, e is the current tree height, and l is the height limit of the tree.
  • First place A in the root node randomly select a dimension q in A, and randomly choose a value p between the maximum and minimum values on q, and flow the sample in A that is larger than p to q to the right.
  • samples smaller than p flow to the left child node. Then repeat the above steps until: each child node has only one sample or multiple identical samples, that is, each sample is isolated, or the height of the tree reaches l.
  • the abnormal point is more easily isolated, so the path length of the leaf node where it is isolated is also shorter, that is, the number of edges experienced by the root node to the leaf node where the abnormal point is located is shorter.
  • the normal point is not easy to be isolated, so its path length is also longer.
  • Step S4 Summarization of abnormal points: Obtain real-time monitoring data of a pollutant in an enterprise in units of hours from the environmental monitoring data system, enter the real-time monitoring data into an isolated forest model corresponding to the pollutants, and achieve The path length from the root node to the leaf node of the isolated forest model determines whether it is an abnormal point, and summarizes the abnormal points.
  • This step is based on the feature that the outliers in the isolated forest model are easier to isolate, and the number of edges experienced by the outlier from the root node to the leaf node where the outlier is located is short.
  • a more accurate isolated forest model is trained by obtaining historical environmental monitoring data from a preset environmental monitoring data system, and the isolated forest model is used to monitor real-time monitoring data, and abnormal points are monitored and summarized.
  • the process monitoring data is simple and fast, and can accurately monitor the excessive situation of various pollution sources in the enterprise and summarize them to prevent the occurrence of excessive standards.
  • step S3 when the isolated forest model is trained using the feature terms in the non-exceeded data set, as shown in FIG. 2, the following method is adopted:
  • a point structure feature is obtained: a feature item in every non-standard data set for every N hours is set as a point structure feature. Since there may be more data in the non-exceeded data set, in order to reduce the use of data, take the feature items in units of the non-exceeded data set for training, that is, you can select the hourly feature items or the feature items every 2 hours to set as Point construction features. These point construction features are put into the root node of the tree of the isolated forest model.
  • a difference threshold is set: a difference between each point structure feature and a previous point structure feature is set to a difference threshold of X.
  • the difference threshold can be randomly generated as the cutting point of the current node.
  • step S303 the left and right child nodes of the tree are constructed: the difference between the structural features of two adjacent points is less than X and is divided into the left child node of the tree, and the difference between X and X is divided into the right child node of the tree.
  • Step S304 recursively construct the tree: recursively, steps S302 and S303, continuously construct left child nodes and right child nodes until the following conditions are met: the training non-exceeded data set has only one record or multiple identical records, or the height of the tree reaches a pre-set Set the height range.
  • the height range of the tree is: in a non-exceeded data set containing n records, the minimum height of the constructed tree is log (n), and the maximum height of the constructed tree is n-1.
  • FIG. 3 for example, four points a, b, c, and d are constructed into an isolated forest model.
  • the four points a, b, c, and d are constructed first.
  • the root node of the tree into the isolated forest model.
  • the difference between the three point structure features a, b, and c and the previous point structure feature is less than X, and is divided into the left child node of the tree.
  • the difference between the point d structure feature and the previous point structure feature is greater than X, and To the right child node of the tree.
  • the above steps are recursively, and the left child node and the right child node are continuously constructed.
  • each leaf node has only one record. It can be seen that the structural feature at point d was isolated at the earliest, so the structural feature at point d is most likely an anomaly.
  • the data in the non-standard data set is first filtered to ensure that the trained isolated forest model is as accurate as possible to reduce the data collection amount.
  • the difference threshold is used to effectively reflect the changes between the structural features of two adjacent points every N hours. The isolated forest model finally obtained can be more reliable as an abnormal point monitoring.
  • step S4 the real-time monitoring data is input into the isolated forest model corresponding to the pollutants, and the path length of the root node to the leaf node of the isolated forest model is determined by the real-time monitoring data to determine whether it is an abnormal point, such as As shown in Figure 4, the following steps are taken:
  • Step S401 generating a path length: real-time data in the real-time monitoring data is input into the corresponding isolated forest model one by one, and the real-time data is divided into M times according to the isolated forest model and is no longer divided, the real-time data is at the root node of the isolated forest model The path length to the leaf node is M.
  • the path length of the structural feature at point a is 2
  • the path length of the structural feature at point b and c is 3
  • the path length of the structural feature at point d is 1.
  • step S402 the normalization process is performed on the path length M of the real-time data to obtain M '.
  • step S403 the abnormal point summary is preset: the abnormal point threshold value Y is preset, and when M 'is greater than Y, the real-time data is set as the abnormal point, and summarized to generate an abnormal point summary table.
  • the range of the abnormal point threshold Y should be greater than 0.5 and close to 1.
  • a normalization method is introduced to normalize the path length of a certain real-time data, and change the path length into a scalar without rigidity, so as to facilitate the summary and sum of the abnormal points. Comparison of abnormal points of each subsequent pollutant.
  • a pollutant abnormality monitoring system is proposed, as shown in FIG. 5, and includes the following units:
  • the acquisition data unit is configured to acquire historical environmental monitoring data of each enterprise's pollution source monitoring point from a preset environmental monitoring data system, and set the historical environmental monitoring data as one pollutant for each enterprise as a data set. storage;
  • the screening unit is set to set an index threshold for each index item of each pollutant, and to filter each data set according to the index threshold, to filter out data that does not exceed the index threshold as a feature item, and to store the feature item in a non-exceeding standard Data set
  • the training unit is set to train the isolated forest model by using the feature items in the non-exceeded data set, and establish a corresponding isolated forest model for each non-exceeded data set;
  • the outlier summary unit is set to obtain real-time monitoring data of a pollutant in an enterprise from the environmental monitoring data system in units of hours, and input the real-time monitoring data into an isolated forest model corresponding to the pollutants.
  • the path length from the root node to the leaf node of the isolated forest model determines whether it is an abnormal point, and summarizes the abnormal points.
  • the pollutants in the data acquisition unit include drainage pollutants based on drainage port monitoring and exhaust pollutants based on exhaust port monitoring.
  • each enterprise stores drainage Pollutant dataset and exhaust pollutant dataset.
  • the index items of drainage pollutants include at least one index item of suspended solids index, chemical oxygen demand index, pH value or ammonia nitrogen index, and corresponding index thresholds are set for the index items of drainage pollutants.
  • the exhaust pollutant index items include at least one of nitrogen oxide index, sulfur dioxide index, soot index or carbon monoxide index, and the exhaust pollutant index items are provided with corresponding index thresholds.
  • the screening unit is further configured to classify and store feature items with one pollutant per enterprise in a corresponding non-exceeding data set.
  • the training unit includes: left and right child node modules of the construction tree, which are set to take the feature items of every N hours in the non-exceeded data set and set as a point construction feature, each point construction feature and the previous point construction feature Set the difference threshold to X, then the difference between the structural features of two adjacent points is less than X is divided into the left child node of the tree, and the difference greater than or equal to X is divided into the right child node of the tree;
  • Recursive module set to recursively construct left and right child nodes until the following conditions are met: the training non-standard data set has only one record or multiple identical records, or the height of the tree reaches a preset height range, and the height range of the tree For: In a non-exceeded data set containing n records, the minimum height of the constructed tree is log (n), and the maximum height of the constructed tree is n-1.
  • the abnormal point summary unit includes:
  • Generate a path length module and set it to input the real-time data in the real-time monitoring data one by one into the corresponding isolated forest model.
  • the real-time data is divided into M times according to the isolated forest model and no longer divided, the real-time data is at the root node of the isolated forest model.
  • the path length to the leaf node is M;
  • the normalization processing module is configured to perform normalization processing on the path length M of the real-time data to obtain M ′;
  • the abnormal point summary table module is set to a preset abnormal point threshold value Y.
  • M ′ is greater than Y
  • the real-time data is set as an abnormal point, and summarized to generate an abnormal point summary table.
  • a computer device which includes a memory and a processor.
  • the memory stores computer-readable instructions.
  • the processor causes the processor to execute the pollutant abnormality in the foregoing embodiments. Steps in a monitoring method.
  • a storage medium storing computer-readable instructions.
  • the one or more processors are caused to execute the pollutant abnormality in each of the foregoing embodiments. Steps in a monitoring method.
  • the storage medium may be a non-volatile storage medium.
  • the program may be stored in a computer-readable storage medium.
  • the storage medium may include: Read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks, etc.

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Abstract

L'invention concerne un procédé et un système de surveillance d'une anomalie de polluant, un dispositif informatique et un support de stockage qui appartiennent au domaine technique du traitement de données de pollution environnementale. Le procédé de surveillance comprend les étapes consistant à : obtenir des données de surveillance d'environnement historiques et les définir sous la forme d'un ensemble de données pour un stockage ; définir des seuils d'indice pour des éléments d'indice d'un polluant, cribler l'ensemble de données selon les seuils d'indice pour obtenir des données ne dépassant pas les seuils d'indice, configurer lesdites données en tant qu'éléments caractéristiques et stocker les éléments caractéristiques dans un ensemble de données ne dépassant pas une norme ; entraîner un modèle forestier d'isolation à l'aide des éléments caractéristiques ; et obtenir des données de surveillance en temps réel, entrer les données de surveillance en temps réel dans le modèle forestier d'isolation et déterminer, en fonction de longueurs de trajet des données de surveillance en temps réel d'un nœud racine à des nœuds feuilles du modèle forestier d'isolation, si lesdites données sont des points anormaux et résumer les points anormaux. Des données de point anormal peuvent être surveillées de manière simple et rapide et la situation dans laquelle une source de pollution dépasse la norme peut être prédite à l'avance et produite, de telle sorte que la situation de dépassement de la norme est empêchée.
PCT/CN2018/106682 2018-07-11 2018-09-20 Procédé et système de surveillance d'une anomalie de polluant, dispositif informatique et support de stockage WO2020010701A1 (fr)

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