WO2021174751A1

WO2021174751A1 - Method, apparatus and device for locating pollution source on basis of big data, and storage medium

Info

Publication number: WO2021174751A1
Application number: PCT/CN2020/104745
Authority: WO
Inventors: 白璟辉; 沈交书
Original assignee: 平安国际智慧城市科技股份有限公司
Priority date: 2020-03-02
Filing date: 2020-07-27
Publication date: 2021-09-10
Also published as: CN111461167B; CN111461167A

Abstract

A method, apparatus and device for locating a pollution source on the basis of big data, and a storage medium. The method comprises: monitoring a target river section region within a pre-configured time period range and collecting data to obtain monitoring data corresponding to multiple sewage outlets; classifying pollutants on the basis of a pre-configured classification model to obtain a pollution source; according to the concentration of pollutants, calculating the pollutant discharge amount corresponding to the pollution source; using a pre-configured algorithm to calculate the correlation between the pollutant discharge amount and the monitoring data, and obtaining a pollutant discharge pattern figure; when detected that the concentration of pollutants exceeds a standard, performing matching analysis on the pollutants exceeding the standard according to the pollutant discharge pattern figure, and obtaining a target pollution source; and when the target pollution source is an industrial source, determining a target company from the pollutant discharge pattern figure, and sending pre-configured warning information. The described method, apparatus, device, and storage medium improve the accuracy of locating a pollution source by means of mining different types of pollution source discharge patterns; moreover, warning is promptly carried out.

Description

Pollution source location method, device, equipment and storage medium based on big data

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on March 2, 2020, with the application number of 202010136439.2. The invention title is "Big Data-Based Pollution Source Location Method, Device, Equipment, and Storage Medium". The entire content of the Chinese patent application Incorporated in the application by reference.

Technical field

This application relates to the field of knowledge relationship mining, and in particular to methods, devices, equipment, and storage media for locating pollution sources based on big data.

Background technique

With the rapid development of society and economy, environmental problems have become increasingly prominent in the process of building smart cities, and water environmental problems are particularly serious. Water pollution control mainly includes four steps: current situation investigation, problem analysis, technology selection, and remediation plan. Through a variety of monitoring methods to achieve comprehensive monitoring of water bodies, discover water pollution problems, obtain scientific remediation plans through technical means, and finally implement and complete water environmental governance.

Traditional investigation methods are commonly used in the traceability of water environment pollution. Traditional investigation methods include deterministic methods and random methods. Among them, the deterministic method is mainly the traceability algorithm of analytical method, factor analysis method, cluster analysis and analytic hierarchy process. The random method is a traceability algorithm based on probability, including a hydrodynamic theory inversion algorithm based on a small amount of monitoring data, a component ratio analysis algorithm based on quantitative monitoring data, a pollution source investigation algorithm based on a large amount of monitoring data, and traceability based on special monitoring methods algorithm.

However, the inventor has realized that finding the source of pollution through traditional investigation methods currently requires a lot of work. At the same time, the single analysis algorithm ignores the superimposed effects of different elements in the actual process of locating pollution sources, resulting in low accuracy of locating pollution sources. In addition, some traceability equipment is used to track, because the application scenarios are mostly sudden water environment alarm events, and the positioning equipment has lagging characteristics, resulting in low efficiency in locating pollution sources.

Summary of the invention

The main purpose of this application is to solve the existing technical problem of low accuracy in locating sources of water environment pollution.

In order to achieve the above objectives, the first aspect of the application provides a method for locating pollution sources based on big data, including: monitoring and collecting data in a target river section within a preset time period to obtain monitoring data corresponding to multiple sewage outlets The monitoring data includes multiple types of pollutants and pollutant concentrations, and each sewage outlet is associated with the target company through a preset unique identifier; the multiple types of pollutants are classified based on the preset classification model to obtain the corresponding The pollution sources include industrial sources, domestic sources, and non-point sources; the pollutant discharge amount corresponding to the pollution source is calculated according to the pollutant concentration, and the pollutant discharge amount includes the pollutant discharge amount of the industrial source, The pollutant discharge amount of the living source and the pollutant discharge amount of the non-point source; the correlation between the pollutant discharge amount and the monitoring data is calculated by a preset algorithm, and the pollution discharge law portrait corresponding to the pollution source is obtained; When it is detected that the concentration of pollutants in the target river reach exceeds the standard, the pollutants exceeding the standard are matched and analyzed according to the pollution discharge pattern image to obtain the target pollution source; when the target pollution source is the industrial source, the pollution discharge law In the portrait, the pollutant discharge outlet corresponding to the pollutant exceeding the standard is queried, the target enterprise that has the illegal discharge behavior is determined according to the preset unique identifier corresponding to the pollutant discharge outlet, and the preset warning information is sent to the target terminal.

The second aspect of this application provides a big data-based pollution source locating device, including: a collection unit for monitoring the target river section within a preset time period and collecting data to obtain monitoring data corresponding to multiple sewage outlets The monitoring data includes multiple types of pollutants and pollutant concentrations, and each sewage outlet is associated with the target company through a preset unique identifier; the classification unit is used to classify the multiple types of pollutants based on a preset classification model. The classification is performed to obtain the corresponding pollution sources. The pollution sources include industrial sources, domestic sources, and non-point sources; the first calculation unit is configured to calculate the pollutant discharge amount corresponding to the pollution source according to the pollutant concentration, and the pollutant discharge The amount includes the pollutant discharge amount of the industrial source, the pollutant discharge amount of the domestic source and the pollutant discharge amount of the non-point source; the second calculation unit is used to calculate the pollutant discharge amount through a preset algorithm The correlation with the monitoring data is used to obtain the pollutant discharge law portrait corresponding to the pollution source; the matching unit, when it is detected that the pollutant concentration in the target river section area exceeds the standard, is used to analyze the pollution that exceeds the standard according to the pollutant discharge law portrait The target pollution source is obtained by matching analysis of the target pollution source; the determining unit, when the target pollution source is the industrial source, is used to query the target discharge outlet corresponding to the pollutant exceeding the standard from the pollution discharge pattern portrait, and according to the target discharge outlet The corresponding preset unique identifier determines the target company that has the illegal row behavior, and sends the preset early warning information to the target terminal.

A third aspect of the present application provides a device for locating pollution sources based on big data, including: a memory and at least one processor, where instructions are stored in the memory, and the memory and the at least one processor are interconnected by wires; The at least one processor calls the instructions in the memory, so that the big data-based pollution source locating device executes the steps of the big data-based pollution source locating method as follows: Monitoring and collecting data in the segment area to obtain monitoring data corresponding to multiple sewage outlets. The monitoring data includes multiple types of pollutants and pollutant concentrations. Each sewage outlet is associated with the target company through a preset unique identifier; Set the classification model to classify the multiple types of pollutants to obtain corresponding pollution sources. The pollution sources include industrial sources, domestic sources, and non-point sources; calculate the pollutant discharge corresponding to the pollution source according to the pollutant concentration, The pollutant discharge amount includes the pollutant discharge amount of the industrial source, the pollutant discharge amount of the domestic source and the pollutant discharge amount of the non-point source; According to the correlation of the monitoring data, the pollutant discharge pattern image corresponding to the pollution source is obtained; when it is detected that the pollutant concentration in the target reach area exceeds the standard, the pollutant exceeding the standard is matched and analyzed according to the pollution discharge pattern image to obtain the target Pollution source; when the target pollution source is the industrial source, query the target sewage outlet corresponding to the pollutant exceeding the standard from the pollution discharge pattern portrait, and determine the existence of sneak discharge according to the preset unique identifier corresponding to the target sewage outlet The target enterprise, and send the preset warning information to the target terminal.

The fourth aspect of the present application provides a computer-readable storage medium having instructions stored in the computer-readable storage medium, which when run on a computer, cause the computer to execute the following big data-based pollution source location method The steps: monitor the target river section within a preset time period and collect data to obtain monitoring data corresponding to multiple sewage outlets. The monitoring data includes multiple types of pollutants and pollutant concentrations, and each sewage outlet Associate with the target enterprise through a preset unique identifier; classify the multiple types of pollutants based on a preset classification model to obtain corresponding pollution sources, and the pollution sources include industrial sources, domestic sources, and non-point sources; according to the pollutants Concentration calculates the pollutant discharge corresponding to the pollution source, the pollutant discharge includes the pollutant discharge of the industrial source, the pollutant discharge of the living source, and the pollutant discharge of the non-point source; The preset algorithm calculates the correlation between the pollutant discharge amount and the monitoring data, and obtains the pollutant discharge law portrait corresponding to the pollution source; when it is detected that the pollutant concentration in the target reach area exceeds the standard, according to the pollutant discharge law The portrait performs matching analysis on the pollutants exceeding the standard to obtain the target pollution source; when the target pollution source is the industrial source, query the target sewage outlet corresponding to the pollutant exceeding the standard from the pollution discharge pattern portrait, and then according to the target pollution source The corresponding preset unique identifier determines the target company that has the illegal row behavior, and sends the preset early warning information to the target terminal.

In the technical solution provided in this application, the target river section area is monitored and data collected within a preset time period to obtain monitoring data corresponding to multiple sewage outlets, and the monitoring data includes multiple types of pollutants and pollutant concentrations , Each sewage outlet is associated with the target company through a preset unique identifier; the multiple types of pollutants are classified based on a preset classification model to obtain corresponding pollution sources, and the pollution sources include industrial sources, domestic sources and non-point sources; Calculate the pollutant discharge amount corresponding to the pollution source according to the pollutant concentration. The pollutant discharge amount includes the pollutant discharge amount of the industrial source, the pollutant discharge amount of the domestic source, and the pollution of the non-point source. The amount of pollutants discharged; the correlation between the amount of pollutants discharged and the monitoring data is calculated by a preset algorithm, and the pollutant discharge pattern corresponding to the pollution source is obtained; when it is detected that the concentration of pollutants in the target reach area exceeds the standard, Perform matching analysis on the pollutants exceeding the standard according to the pollution discharge pattern portrait to obtain the target pollution source; when the target pollution source is the industrial source, query the target pollution outlet corresponding to the pollutant exceeding the standard from the pollution discharge pattern portrait, according to The preset unique identifier corresponding to the target sewage outlet determines the target enterprise that has the illegal discharge behavior, and sends the preset early warning information to the target terminal. In the embodiments of this application, through the use of massive data for big data calculations, different types of pollution source emission rules are mined, and pollution emission rules images are obtained. The pollution source location is performed based on the pollution emission rules images, which improves the accuracy of pollution source location, quickly locates problems and early warnings. .

Description of the drawings

FIG. 1 is a schematic diagram of an embodiment of a method for locating a pollution source based on big data in an embodiment of the application;

2 is a schematic diagram of another embodiment of a method for locating a pollution source based on big data in an embodiment of this application;

FIG. 3 is a schematic diagram of an embodiment of a device for locating a pollution source based on big data in an embodiment of the application;

FIG. 4 is a schematic diagram of another embodiment of a device for locating a pollution source based on big data in an embodiment of the application;

FIG. 5 is a schematic diagram of an embodiment of a device for locating a pollution source based on big data in an embodiment of the application.

Detailed ways

The embodiments of the application provide a method, device, equipment, and storage medium for locating pollution sources based on big data, which are used to dig out different types of pollution source emission laws by using massive data to perform big data calculations, and obtain pollution emission laws portraits, based on pollution emission Regular portraits are used to locate pollution sources, improve the accuracy of pollution source locating, quickly locate problems and give early warnings in time.

In order to enable those skilled in the art to better understand the solution of the present application, the embodiments of the present application will be described below in conjunction with the accompanying drawings in the embodiments of the present application.

The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of this application and the above-mentioned drawings are used to distinguish similar objects, without having to use To describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in a sequence other than the content illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those clearly listed. Steps or units, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products, or equipment.

For ease of understanding, the following describes the specific process of the embodiment of the present application. Please refer to FIG. 1. An embodiment of the method for locating a pollution source based on big data in the embodiment of the present application includes:

101. Monitor the target river section within the preset time period and collect data to obtain monitoring data corresponding to multiple sewage outlets. The monitoring data includes multiple types of pollutants and pollutant concentrations, and each sewage outlet passes the preset The unique identifier is associated with the target company;

The server monitors the target river section within the preset time period and collects data, and obtains monitoring data corresponding to multiple sewage outlets. The monitoring data includes multiple types of pollutants and pollutant concentrations, and each sewage outlet is unique through the preset The identification is associated with the target company. Among them, the preset duration range can be one day, one week, one month, and one year, which is not specifically limited here. There is a one-to-one correspondence between the sewage outlet and the target company. For example, the sewage outlet A is associated with the target company A through a preset unique identifier 10, where the preset unique identifier can also be a character string set according to a universal unique identification code, specifically here Not limited. The multiple types of pollution sources in the target river section mainly include sewage discharge from industrial sources, direct sewage discharge from domestic sources, and rainwater runoff discharge from urban non-point sources. Under normal circumstances, multiple types of pollution sources are relatively fixed. They change with time and are affected by climatic factors. The relationship between pollution source outlets and rivers is established through the city’s pipeline network to determine the relationship between multiple types of pollution sources and target companies. . For example, the discharge outlet of the target company's workshop or the total discharge outlet of the target company can be treated, and the specifics are not limited here.

It is understandable that the execution subject of this application may be a pollution source locating device based on big data, or may also be a terminal or a server, which is not specifically limited here. The embodiment of the present application takes the server as the execution subject as an example for description.

102. Classify multiple types of pollutants based on a preset classification model to obtain corresponding pollution sources. The pollution sources include industrial sources, domestic sources, and non-point sources;

The server classifies multiple types of pollutants based on a preset classification model to obtain corresponding pollution sources. The pollution sources include industrial sources, living sources, and non-point sources. Among them, the preset classification model is a pre-trained classification model, and the server classifies multiple types of pollutants according to the preset classification model to obtain industrial sources, living sources, and non-point sources. For example, the main pollutants from domestic sources include ammonia nitrogen and total phosphorus, and the main pollutants from industrial sources and non-point sources include heavy metals.

It should be noted that non-point source is a phenomenon in which air and surface pollutants are brought into the receiving water body through rainfall and surface runoff, and the receiving water body is polluted. The pollution caused by the non-point source is discharged through the drainage pipe network. Due to the rainfall runoff, the pollutants deposited on the surface and deposited in the sewage pipe network will suddenly scour into the receiving water body within the preset time period, causing water pollution. For example, in the 20 minutes before the initial rainstorm, the pollutant concentration generally exceeds the usual sewage concentration. Therefore, during rainfall, non-point source is the main pollution source that causes water pollution.

103. Calculate the pollutant discharge corresponding to the pollution source based on the pollutant concentration. The pollutant discharge includes the pollutant discharge from industrial sources, the pollutant discharge from domestic sources, and the pollutant discharge from non-point sources;

The server calculates the pollutant discharge amount corresponding to the pollution source according to the pollutant concentration. The pollutant discharge amount includes the pollutant discharge amount of the industrial source, the pollutant discharge amount of the domestic source and the pollutant discharge amount of the non-point source. Further, the server determines the pollutant discharge amount according to the different calculation methods of the pollutant discharge amount of industrial sources, the pollutant discharge amount of domestic sources, and the pollutant discharge amount of non-point source, and the corresponding pollutant concentration of each. Among them, the amount of pollutant discharge refers to the amount of certain pollutants discharged into the environment or other facilities by the pollution source, including the pollution discharge amount of various types of pollutants discharged into the water environment. Pollutant concentration refers to the amount of pollutants contained in a unit volume. For example, by multiplying the pollutant concentration and wastewater discharge volume, the pollutant discharge volume from industrial sources can be obtained.

104. Calculate the correlation between pollutant discharge and monitoring data through a preset algorithm, and obtain a portrait of the pollution discharge law corresponding to the pollution source;

The server calculates the correlation between pollutant emissions and monitoring data through a preset algorithm, and obtains a portrait of the pollution discharge law corresponding to the pollution source. Among them, the relevance includes the classification of various pollution sources according to the time period. For example, between 12:00-13:00 noon, the population is concentrated in commercial areas to eat, and the concentration of pollutants corresponding to living sources rises, leading to an increase in pollutant emissions. It is understandable that the server finally determines the normal range of pollution discharge of various pollution sources by combining water quality standards to ensure that the water quality of the river in the target river section does not exceed the standard. The normal intervals of pollution discharge of various pollution sources are time-series dynamic, and finally a picture of the pollution discharge law of multiple pollution sources is generated.

105. When it is detected that the concentration of pollutants in the target river section exceeds the standard, the pollutants exceeding the standard are matched and analyzed from the pollution discharge law portrait to obtain the target pollution source;

When it is detected that the concentration of pollutants in the target river section exceeds the standard, the server will perform matching analysis on the pollutants exceeding the standard from the pollution discharge pattern image to obtain the target pollution source. Specifically, the server determines the pollutants that exceed the standard; the server analyzes and extracts the pollutants that exceed the standard through the pollutant discharge pattern portrait to obtain the target pollution source corresponding to the pollutant; when the number of target pollution sources is greater than 1, the target pollution source with the maximum pollutant concentration is determined Set as the final target pollution source. For example, the pollutants from life sources mainly come from vegetable washing water, dishwashing water and clean and sanitary water, including animal and vegetable oils, protein, cellulose and phosphorus. The corresponding pollutants can be classified as animal and vegetable oils. , Chemical oxygen demand, ammonia nitrogen, total phosphorus, when the pollutants are ammonia nitrogen and total phosphorus, and the concentration of ammonia nitrogen and total phosphorus exceeds the standard, the server determines the target pollution source corresponding to the pollutant as the living source through the pollutant discharge law portrait.

106. When the target pollution source is an industrial source, query the target discharge outlet corresponding to the pollutant that exceeds the standard from the pollution discharge pattern portrait, and determine the target enterprise that has sneaked discharge according to the preset unique identifier corresponding to the target discharge outlet, and send a preset warning Information to the target terminal.

When the target pollution source is an industrial source, the server queries the target sewage outlets corresponding to the pollutants that exceed the standard from the pollution discharge pattern portrait, and determines the target companies that have sneaked discharges based on the preset unique identifiers corresponding to the target sewage outlets, and sends preset warning messages To the target terminal. Through the correlation between the concentration of various pollutants and the pollution emission of pollution sources, determine the proportion of the pollution emission of various pollution sources corresponding to the concentration of various pollutants. That is, when the concentration of pollutants exceeds the standard and the pollution source corresponding to the pollutant is an industrial source, according to the discharge amount of various pollution sources, combined with the portrait of pollution discharge rules, judge whether it is in the normal range, so as to realize the precise location of the pollution source.

In the embodiments of this application, through the use of massive data for big data calculations, different types of pollution source emission rules are mined, and pollution emission rules images are obtained. The pollution source location is performed based on the pollution emission rules images, which improves the accuracy of pollution source location, quickly locates problems and early warnings. .

Referring to FIG. 2, another embodiment of the method for locating a pollution source based on big data in the embodiment of the present application includes:

201. Monitor the target river section within the preset time period and collect data to obtain monitoring data corresponding to multiple sewage outlets. The monitoring data includes multiple types of pollutants and pollutant concentrations, and each sewage outlet passes the preset The unique identifier is associated with the target company;

The server monitors the target river section within the preset time period and collects data, and obtains monitoring data corresponding to multiple sewage outlets. The monitoring data includes multiple types of pollutants and pollutant concentrations, and each sewage outlet is unique through the preset The identification is associated with the target company. Among them, the preset duration range can be one day, one week, one month, and one year, which is not specifically limited here. There is a one-to-one correspondence between the sewage outlet and the target company. For example, the sewage outlet A is associated with the target company A through a preset unique identifier 10.

It is understandable that the sampling frequency and sampling time of multiple sewage outlets are determined by investigating the pollutant discharge methods and discharge rules; the sampling locations and the number of sampling points of multiple sewage outlets are determined by investigating the location and number of pollution sources. .

202. Classify multiple types of pollutants based on a preset classification model to obtain corresponding pollution sources. The pollution sources include industrial sources, domestic sources, and non-point sources;

The server classifies multiple types of pollutants based on a preset classification model to obtain corresponding pollution sources. The pollution sources include industrial sources, living sources, and non-point sources. Among them, the preset classification model is a pre-trained classification model, and the server classifies multiple types of pollutants according to the preset classification model to obtain industrial sources, living sources, and non-point sources.

Specifically, the server queries the preset pollution data of the target enterprise from the preset database, and determines the types of pollutants produced and discharged by multiple pollution sources and the range of pollutants concentration from the preset pollution data to obtain the first pollutant data set; The server obtains the published pollutants in the target river section and sets the published pollutants as the second pollutant data set; the server fuses the first pollutant data set and the second pollutant data set, and performs the fusion The pollutant data set identifies the pollution source, and the pollutant sample library is obtained. The pollution sources include industrial sources, domestic sources and non-point sources; the server trains the initial classification model according to the pollutant sample library to obtain a preset classification model, where the initial classification model can be k nearest neighbor KNN classifier; the server matches and recognizes multiple types of pollutants according to the preset classification model, and obtains the pollution sources corresponding to the pollutants. For example, the main pollutants of domestic sources include ammonia nitrogen and total phosphorus, industrial sources and non-point sources The main pollutants include heavy metals.

203. Obtain the waste water discharge volume, and multiply the pollutant concentration and the waste water discharge volume to calculate the pollutant discharge volume from the industrial source;

The server obtains the waste water discharge volume, and multiplies the pollutant concentration and the waste water discharge volume to obtain the pollutant discharge volume of the industrial source, that is, the pollutant discharge volume of the industrial source = the pollutant concentration * the waste water discharge volume. Among them, the amount of pollutant discharge refers to the amount of certain pollutants discharged into the environment or other facilities by the pollution source, including the pollution discharge amount of various types of pollutants discharged into the water environment. Pollutant concentration refers to the amount of pollutants contained in a unit volume.

204. Obtain population distribution data, and multiply the population distribution data and the concentration of pollutants to obtain the pollution emissions from domestic sources;

The server obtains the population distribution data, and multiplies the population distribution data and the pollutant concentration to obtain the pollution discharge amount of the living source, that is, the pollutant discharge amount of the living source = population distribution data * pollutant discharge concentration coefficient. Specifically, the server determines the population distribution data through the collected massive mobile phone signaling data, and the server obtains the pollutant emission concentration coefficient according to the geographical characteristics of the area where the population distribution data is located within a preset time period. The geographical characteristics of the area include residential areas, Commercial areas and public areas; the server multiplies the population distribution data and the pollutant emission concentration coefficient to obtain the pollution emissions from domestic sources.

205. Obtain rainfall data, and estimate the discharge of non-point source pollutants based on the rainfall data. The rainfall data includes preset runoff coefficient, preset rainwater runoff and pollutant concentration;

The server obtains rainfall data, and estimates the pollutant emission of non-point source based on the rainfall data. The rainfall data includes preset runoff coefficient, preset rainwater runoff and pollutant concentration, that is: non-point source pollutant emission = preset runoff Coefficient * preset street dust pollutant concentration * rainwater runoff. Specifically, the server obtains the preset runoff coefficients, preset rainwater runoff and pollutant concentration of various types of land in the target area from the preset land use distribution data within the preset time period; the server calculates the non-point source pollution based on the rainfall data Emissions.

206. Calculate the correlation between pollutant discharge and monitoring data through a preset algorithm, and obtain a portrait of the pollution discharge law corresponding to the pollution source;

The server calculates the correlation between pollutant emissions and monitoring data through a preset algorithm, and obtains a portrait of the pollution discharge law corresponding to the pollution source. Among them, the relevance includes the classification of various pollution sources according to the time period. Specifically, first, the server serializes the pollutant emissions corresponding to multiple pollution sources according to the time sequence, and obtains the time series of various pollutant emissions. The time series includes three dimensions, namely the x-axis coordinate and the y-axis. Coordinates and emissions, according to different seasons and climates, the time series of different pollutant emissions corresponding to different pollution sources are also different.

Secondly, the server draws the time series emission laws of various pollution sources and calculates correlation coefficients for the time series of multiple types of pollutants, pollutant concentrations, and emissions of various pollutants through a preset algorithm, and converts the correlation coefficients into weights. The setting algorithm includes the Pearson correlation coefficient algorithm. Further, the server draws the time series emission rules of various pollutants for the time series of multiple types of pollutants, pollutant concentrations, and discharge amounts of different pollutants through a preset algorithm, and the time series emission laws are correlation scatter diagrams; The Pearson correlation coefficient algorithm is used to calculate the correlation coefficients between the discharge of various pollutants and the concentration of pollutants according to the time series emission law; the server calculates the comprehensive discharge of pollutants corresponding to various pollution sources according to the discharge amounts and correlation coefficients of various pollutants. The pollutant weights of various pollution sources are obtained, and the pollutant weights are used to indicate the proportion of pollutant discharges of various pollution sources. For example, the server determines that the corresponding water environment monitoring concentration value floats according to the daily industrial pollution source production and discharge law; the server determines the corresponding water environment monitoring concentration value float according to the daily life pollution discharge caused by the daily population activity law; the server determines the corresponding water environment monitoring concentration value fluctuation; The pollution discharge of rainwater runoff caused by field rainfall increases, and the corresponding increase in the concentration of water environment monitoring is determined.

Finally, the server generates a pollutant discharge pattern portrait based on the time series discharge rules of various pollution sources, multiple discharge outlets and corresponding target companies. Among them, the pollution discharge pattern portrait includes the main pollutants involved in the pollution source corresponding to each discharge outlet and the discharge in the time series. Interval, for example, the main pollutants from domestic sources include ammonia nitrogen and total phosphorus, and the main pollutants from industrial sources and non-point sources include heavy metals. The server uses massive data combined with time-series characteristics to generate pollution emission law portraits, while also considering weather factors and demographic factors, such as the impact of rainfall on non-point sources, the impact of changes in human flow on living sources, and the impact of industrial activities on industrial sources.

207. When it is detected that the concentration of pollutants in the target river section exceeds the standard, the pollutants exceeding the standard are matched and analyzed according to the pollution discharge law portrait to obtain the target pollution source;

When it is detected that the concentration of pollutants in the target river section exceeds the standard, the server will perform a matching analysis on the pollutants exceeding the standard according to the pollution discharge pattern image to obtain the target pollution source. Specifically, the server determines the pollutants that exceed the standard; the server analyzes and extracts the pollutants that exceed the standard through the pollutant discharge pattern portrait to obtain the target pollution source corresponding to the pollutant; when the number of target pollution sources is greater than 1, the target pollution source with the maximum pollutant concentration is determined Set as the final target pollution source. For example, the pollutants from life sources mainly come from vegetable washing water, dishwashing water and clean and sanitary water, including animal and vegetable oils, protein, cellulose and phosphorus. The corresponding pollutants can be classified as animal and vegetable oils. , Chemical oxygen demand, ammonia nitrogen, total phosphorus, when the pollutants are ammonia nitrogen and total phosphorus, and the concentration of ammonia nitrogen and total phosphorus exceeds the standard, the server determines the pollution source corresponding to the pollutant as the source of life through the pollution law portrait.

208. When the target pollution source is an industrial source, query the target discharge outlet corresponding to the pollutant that exceeds the standard from the pollution discharge pattern portrait, and determine the target enterprise that has sneaked discharge according to the preset unique identifier corresponding to the target discharge outlet, and send a preset warning Information to the target terminal.

When the target pollution source is an industrial source, query the target discharge outlet corresponding to the pollutant that exceeds the standard from the pollution discharge pattern portrait, and determine the target company that has sneaked discharge according to the preset unique identifier corresponding to the target discharge outlet, and send the preset warning information to Target terminal. Specifically, when the pollution source corresponding to the pollutant is an industrial source, the server calculates the real-time emission amount according to the pollutant concentration and the weight of the pollutant; the server determines the standard emission amount corresponding to the pollutant through the pollutant discharge law portrait; the server judges whether the real-time emission amount is greater than the standard Emissions; if the real-time emissions are greater than the standard emissions, the server will determine the corresponding target outlets through the portrait of pollution emission laws, and get the preset unique identifiers corresponding to the target outlets; the server will query it according to the preset unique identifications corresponding to the target outlets Target company: The server generates preset warning information for the target company and sends the preset warning information to the target terminal.

Optionally, the server judges whether the target company has illegal discharge or data fraud based on the portrait of material conservation and pollution discharge laws. Specifically, the server obtains the water and electricity consumption data of the target company and the actual pollutant discharge volume of the target company; calculates the theoretical pollutant production volume according to the water and electricity consumption data and the pollution production coefficient of the target company, and the pollution production coefficient is used to indicate the prior basis Determine the preset production facilities and preset materials of the target enterprise; multiply the amount of pollutants generated by the preset emission coefficient to obtain the theoretical emission amount of pollutants. The preset emission coefficient is the data obtained in advance based on the preset mass data. Interval; when it is determined through material conservation that the amount of pollutants produced is less than the actual emissions of the target company or the theoretical emissions of pollutants are greater than the actual emissions of pollutants, it is determined that the target company has data fraud; when the target company is determined according to the portrait of the pollutant discharge law When the actual emissions are abnormal or the actual emissions of the target company are greater than the theoretical emissions of pollutants, it is determined that the target company has illegal emissions.

It is understandable that this assessment method can focus on target companies with poor environmental credit, target companies with high environmental risks, and target companies with complaints, law enforcement, and punishment. When the target company is detected as illegally arranged and data fraudulent, Push information to target personnel in real time to achieve measurement and management coordination. The target personnel includes law enforcement personnel, which greatly improves the work efficiency of law enforcement personnel and solves the problem of low accuracy in locating the source of pollution sources.

The pollution source locating method based on big data in the embodiment of this application is described above, and the pollution source locating device based on big data in the embodiment of this application is described below. Please refer to FIG. 3, the pollution source locating device based on big data in the embodiment of this application An example of includes:

The collection unit 301 is used to monitor the target river section within a preset time period and collect data to obtain monitoring data corresponding to multiple sewage outlets. The monitoring data includes multiple types of pollutants and the concentration of pollutants. The port is associated with the target company through a preset unique identifier;

The classification unit 302 is configured to classify multiple types of pollutants based on a preset classification model to obtain corresponding pollution sources. The pollution sources include industrial sources, domestic sources, and non-point sources;

The first calculation unit 303 is configured to calculate the pollutant discharge amount corresponding to the pollution source according to the pollutant concentration. The pollutant discharge amount includes the pollutant discharge amount of industrial sources, the pollutant discharge amount of domestic sources, and the pollutant discharge amount of non-point source;

The second calculation unit 304 is configured to calculate the correlation between the pollutant discharge amount and the monitoring data through a preset algorithm, and obtain the pollution discharge law portrait corresponding to the pollution source;

The matching unit 305, when it is detected that the concentration of pollutants in the target river section exceeds the standard, is used to perform matching analysis on the pollutants exceeding the standard according to the pollution discharge law portrait to obtain the target pollution source;

The determining unit 306, when the target pollution source is an industrial source, is used to query the target sewage outlet corresponding to the pollutant exceeding the standard from the pollution discharge pattern portrait, and determine the target enterprise that has the illegal discharge behavior according to the preset unique identifier corresponding to the target sewage outlet, and Send preset warning information to the target terminal.

Referring to FIG. 4, another embodiment of the device for locating pollution sources based on big data in the embodiment of the present application includes:

Optionally, the classification unit 302 may also be specifically configured to:

Obtain the preset pollution data of the target company, and determine the pollutant type and the range of pollutant concentration from the preset pollution data to obtain the first pollutant data set;

Obtain the published pollutants in the target reach and set the published pollutants as the second pollutant data set;

The first pollutant data set and the second pollutant data set are merged, and the pollution source is identified for the merged dye data set to obtain a pollutant sample library. The pollution sources include industrial sources, domestic sources and non-point sources;

Train the initial classification model according to the pollutant sample library to obtain the preset classification model;

According to the preset classification model, multiple types of pollutants are matched and identified, and the corresponding pollution source is obtained.

Optionally, the first calculation unit 303 may also be specifically configured to:

Obtain the wastewater discharge volume, and multiply the pollutant concentration and wastewater discharge volume to calculate the pollutant discharge volume from industrial sources;

Obtain population distribution data, and multiply it according to population distribution data and pollutant concentration to obtain the pollution emissions from domestic sources;

Obtain rainfall data, and estimate the discharge of non-point source pollutants based on the rainfall data. The rainfall data includes preset runoff coefficient, preset rainwater runoff, and pollutant concentration.

Optionally, the second calculation unit 304 may further include:

The processing sub-unit 3041 is used to serialize the pollutant discharge amount according to the time sequence to obtain the time sequence of the pollutant discharge amount;

The drawing sub-unit 3042 is used to draw the time series emission law of pollution sources and calculate the correlation coefficient for the time series sequence of multiple types of pollutants, pollutant concentrations and pollutant emissions through a preset algorithm, and convert the correlation coefficients into weights. The setting algorithm includes Pearson's correlation coefficient algorithm;

The generation sub-unit 3043 generates a pollution discharge rule portrait according to the time series discharge rule of the pollution source, multiple sewage outlets and the target enterprise.

Optionally, the drawing subunit 3042 may also be specifically used for:

Draw the time series emission law of pollutants for the time series of multiple types of pollutants, pollutant concentration and pollutant discharge amount through the preset algorithm, and the time series emission law is a correlation scatter diagram;

Calculate the correlation coefficient between pollutant emission and pollutant concentration through Pearson's correlation coefficient algorithm according to the time series emission law;

According to the pollutant discharge volume and the correlation coefficient, the comprehensive pollutant discharge volume corresponding to the pollution source is calculated, and the pollutant weight of the pollution source is obtained. The pollutant weight is used to indicate the proportion of the pollutant discharge of the pollution source.

Optionally, the determining unit 306 may also be specifically configured to:

When the pollution source corresponding to the pollutant is an industrial source, calculate the real-time emissions according to the pollutant concentration and pollutant weight;

Determine the standard discharge amount of pollutants corresponding to the pollutant discharge law portrait;

Determine whether the real-time emissions are greater than the standard emissions;

If the real-time discharge volume is greater than the standard discharge volume, the corresponding target discharge outlet is determined through the portrait of the pollution discharge law, and the preset unique identification corresponding to the target discharge outlet is obtained;

Query the target enterprise according to the preset unique identifier corresponding to the target sewage outlet;

Generate preset warning information for the target company, and send the preset warning information to the target terminal.

Optionally, the device for locating pollution sources based on big data further includes:

The obtaining unit 307 is used to obtain the water and electricity data of the target company and the actual discharge amount of pollutants of the target company;

The third calculation unit 308 is used to calculate the theoretical pollutant production amount based on the water and electricity data and the pollution production coefficient of the target company. The pollution production coefficient is used to indicate that the target company’s preset production facilities and preset materials are determined in advance. ；

The calculation unit 309 multiplies the amount of pollutants produced by a preset emission coefficient to obtain a theoretical emission amount of pollutants, and the preset emission coefficient is a data interval obtained by pre-calculation based on preset massive data;

The first processing unit 310, when it is determined through material conservation that the amount of pollutants generated is less than the actual discharge amount of the target company or the theoretical discharge amount of pollutants is greater than the actual discharge amount of the pollutants, determines that the target company has data fraud;

The second processing unit 311 determines that the target company has an illegal discharge behavior when it is determined that the actual discharge volume of the target company is abnormal discharge or the actual discharge volume of the target company is greater than the theoretical discharge volume of pollutants based on the pollution discharge law profile.

The above figures 3 and 4 describe in detail the pollution source locating device based on big data in the embodiment of this application from the perspective of modular functional entities. The following describes the pollution source locating device based on big data in the embodiment of this application in detail from the perspective of hardware processing. describe.

FIG. 5 is a schematic structural diagram of a big data-based pollution source locating device provided by an embodiment of the present application. The big data-based pollution source locating device 500 may have relatively large differences due to different configurations or performances, and may include one or more A processor (central processing units, CPU) 501 (for example, one or more processors), a memory 509, and one or more storage media 508 (for example, one or more storage devices with a large amount of data) storing application programs 507 or data 506. Among them, the memory 509 and the storage medium 508 may be short-term storage or persistent storage. The program stored in the storage medium 508 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the device for locating pollution sources based on big data. Further, the processor 501 may be configured to communicate with the storage medium 508, and execute a series of instruction operations in the storage medium 508 on the pollution source locating device 500 based on big data.

The pollution source location device 500 based on big data may also include one or more power supplies 502, one or more wired or wireless network interfaces 503, one or more input and output interfaces 504, and/or one or more operating systems 505, For example, Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that the structure of the pollution source locating device based on big data shown in FIG. 5 does not constitute a limitation on the pollution source locating device based on big data, and may include more or less components than shown in the figure, or a combination Certain components, or different component arrangements.

This application also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium, and the computer-readable storage medium may also be a volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program runs on a computer, the computer is caused to execute the steps of the method for locating a pollution source based on big data.

Those skilled in the art can clearly understand that, for the convenience and conciseness of the description, the specific working process of the above-described system, device, and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed system, device, and method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or It can be integrated into another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions recorded in the embodiments are modified, or some of the technical features are equivalently replaced; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

A method for locating pollution sources based on big data, which includes:

Monitor the target river section within the preset time period and collect data to obtain monitoring data corresponding to multiple sewage outlets. The monitoring data includes multiple types of pollutants and pollutant concentrations. Each sewage outlet passes the preset The unique identifier is associated with the target company;

Classify the multiple types of pollutants based on a preset classification model to obtain corresponding pollution sources, where the pollution sources include industrial sources, living sources, and non-point sources;

Calculate the pollutant discharge amount corresponding to the pollution source according to the pollutant concentration. The pollutant discharge amount includes the pollutant discharge amount of the industrial source, the pollutant discharge amount of the domestic source, and the pollution of the non-point source. Emissions

Calculate the correlation between the pollutant discharge amount and the monitoring data through a preset algorithm, and obtain the pollutant discharge law portrait corresponding to the pollution source;

When it is detected that the concentration of pollutants in the target river section exceeds the standard, matching analysis is performed on the pollutants exceeding the standard according to the pollution discharge law profile to obtain the target pollution source;

When the target pollution source is the industrial source, query the target sewage outlet corresponding to the pollutant that exceeds the standard from the pollution discharge pattern portrait, and determine the target enterprise that has the illegal discharge behavior according to the preset unique identifier corresponding to the target sewage outlet , And send the preset warning information to the target terminal.
The method for locating pollution sources based on big data according to claim 1, wherein the multiple types of pollutants are classified based on a preset classification model to obtain corresponding pollution sources, and the pollution sources include industrial sources and domestic sources. And non-point sources, including:

Acquiring preset pollution data of the target enterprise, and determining the type of pollutant and the range of pollutant concentration from the preset pollution data to obtain a first pollutant data set;

Acquiring published pollutants in the target river section area, and setting the published pollutants as a second pollutant data set;

Fuse the first pollutant data set and the second pollutant data set, and identify the pollution source of the fused dye data set to obtain a pollutant sample library, and the pollution sources include industrial sources, domestic sources, and non-point sources;

Training the initial classification model according to the pollutant sample library to obtain a preset classification model;

Matching and identifying the multiple types of pollutants according to the preset classification model to obtain corresponding pollution sources.
The method for locating pollution sources based on big data according to claim 1, wherein the pollutant discharge amount corresponding to the pollution source is calculated according to the pollutant concentration, and the pollutant discharge amount includes pollutants from the industrial source The discharge volume, the pollutant discharge volume of the living source and the pollutant discharge volume of the non-point source include:

Obtain the waste water discharge volume, and perform multiplication calculation on the pollutant concentration and the waste water discharge volume to obtain the pollutant discharge volume of the industrial source;

Obtaining population distribution data, and performing multiplication operations based on the population distribution data and the pollutant concentration to obtain the pollution discharge amount of the living source;

Obtain rainfall data, and estimate the pollutant discharge amount of the non-point source according to the rainfall data. The rainfall data includes a preset runoff coefficient, a preset rainwater runoff, and the pollutant concentration.
The method for locating pollution sources based on big data according to claim 1, wherein the calculation of the correlation between the pollutant discharge amount and the monitoring data through a preset algorithm to obtain the pollution discharge law portrait corresponding to the pollution source includes :

Serialize the pollutant discharge amount according to time sequence to obtain a time series sequence of the pollutant discharge amount;

Draw the time series emission law of the pollution source and calculate the correlation coefficient for the time series sequence of the multiple types of pollutants, the concentration of the pollutants and the discharge amount of the pollutants through a preset algorithm, and convert the correlation coefficients into weights, The preset algorithms include Pearson's correlation coefficient algorithm;

According to the time-series discharge rule of the pollution source, the plurality of sewage outlets and the target enterprise, a pollution discharge rule portrait is generated.
The method for locating pollution sources based on big data according to claim 4, wherein the time series of the multiple types of pollutants, the pollutant concentration, and the pollutant discharge amount are drawn by a preset algorithm. Describe the time series emission law of pollution sources and calculate the correlation coefficients, and convert the correlation coefficients into weights. The preset algorithms include the Pearson correlation coefficient algorithm, including:

Draw the time series emission law of the pollutants for the time series of the multiple types of pollutants, the concentration of the pollutants, and the discharge amount of the pollutants through a preset algorithm, and the time series emission laws are the correlation scatter points picture;

Calculate the correlation coefficient between the pollutant emission amount and the pollutant concentration according to the time series emission rule by using the Pearson correlation coefficient algorithm;

Calculate the comprehensive pollutant discharge corresponding to the pollution source according to the pollutant discharge volume and the correlation coefficient to obtain the pollutant weight of the pollution source, and the pollutant weight is used to indicate the proportion of the pollutant discharge of the pollution source .
The method for locating pollution sources based on big data according to claim 5, wherein, when the target pollution source is the industrial source, the target pollution outlet corresponding to the pollutant exceeding the standard is queried from the pollution discharge pattern portrait, according to The preset unique identifier corresponding to the target sewage outlet determines the target company that has the illegal discharge behavior, and sends the preset warning information to the target terminal, including:

When the pollution source corresponding to the pollutant is the industrial source, calculating the real-time emission amount according to the pollutant concentration and the pollutant weight;

Determining the standard discharge amount of the pollutant corresponding to the pollutant through the portrait of the pollution discharge law;

Judging whether the real-time emission amount is greater than the standard emission amount;

If the real-time emission amount is greater than the standard emission amount, the corresponding target sewage outlet is determined through the pollution emission law portrait, and the preset unique identifier corresponding to the target sewage outlet is obtained;

Query to obtain the target enterprise according to the preset unique identifier corresponding to the target sewage outlet;

Generate preset early warning information for the target enterprise, and send the preset early warning information to the target terminal.
The method for locating pollution sources based on big data according to any one of claims 1-6, wherein, when the target pollution source is the industrial source, the corresponding pollutants exceeding the standard are queried from the pollution discharge pattern portrait. After determining the target enterprise that has sneaked discharge behavior according to the preset unique identifier corresponding to the target sewage outlet, and sending the preset warning information to the target terminal, the big data-based pollution source location method further includes:

Obtain the water and electricity consumption data of the target company and the actual pollutant discharge volume of the target company;

The theoretical amount of pollutants produced is calculated according to the water and electricity data and the pollution production coefficient of the target enterprise, and the pollution production coefficient is used to indicate that it is determined in advance based on the target enterprise's preset production facilities and preset materials ；

Multiplying the amount of pollutants produced by a preset emission coefficient to obtain a theoretical pollutant emission amount, where the preset emission coefficient is a data interval obtained by pre-calculation based on preset mass data;

When it is determined through the conservation of materials that the amount of pollutants produced is less than the actual discharge amount of the target company or the theoretical discharge amount of the pollutants is greater than the actual discharge amount of the pollutants, it is determined that the target company has data fraud;

When it is determined that the actual emission volume of the target company is abnormal discharge or the actual emission volume of the target company is greater than the theoretical discharge volume of pollutants according to the pollution discharge law profile, it is determined that the target company has an illegal discharge behavior.
A device for locating pollution sources based on big data, wherein the device for locating pollution sources based on big data includes:

The collection unit is used to monitor the target river section area within a preset time length and collect data to obtain monitoring data corresponding to multiple sewage outlets. The monitoring data includes multiple types of pollutants and pollutant concentrations, each The sewage outlet is associated with the target company through a preset unique identifier;

The classification unit is configured to classify the multiple types of pollutants based on a preset classification model to obtain corresponding pollution sources, and the pollution sources include industrial sources, domestic sources, and non-point sources;

The first calculation unit is configured to calculate the pollutant discharge amount corresponding to the pollution source according to the pollutant concentration, and the pollutant discharge amount includes the pollutant discharge amount of the industrial source and the pollutant discharge amount of the domestic source And the amount of pollutants discharged from the non-point source;

The second calculation unit is configured to calculate the correlation between the pollutant discharge amount and the monitoring data through a preset algorithm to obtain the pollution discharge law portrait corresponding to the pollution source;

The matching unit, when it is detected that the concentration of pollutants in the target river section exceeds the standard, is used to perform matching analysis on the pollutants exceeding the standard according to the pollution discharge law profile to obtain the target pollution source;

The determining unit, when the target pollution source is the industrial source, is used to query the target pollution outlet corresponding to the pollutant exceeding the standard from the pollution discharge pattern portrait, and determine the existence of theft according to the preset unique identifier corresponding to the target pollution outlet Arrange the target companies of the behavior, and send the preset warning information to the target terminal.
A device for locating pollution sources based on big data, wherein the device for locating pollution sources based on big data includes: a memory and at least one processor, the memory stores instructions, and the memory and the at least one processor pass through a line interconnection;

The at least one processor invokes the instructions in the memory, so that the big data-based pollution source locating device executes the steps of the big data-based pollution source locating method as described below:

Monitor the target river section within the preset time period and collect data to obtain monitoring data corresponding to multiple sewage outlets. The monitoring data includes multiple types of pollutants and pollutant concentrations. Each sewage outlet passes the preset The unique identifier is associated with the target company;

Classify the multiple types of pollutants based on a preset classification model to obtain corresponding pollution sources, where the pollution sources include industrial sources, living sources, and non-point sources;

Calculate the pollutant discharge amount corresponding to the pollution source according to the pollutant concentration. The pollutant discharge amount includes the pollutant discharge amount of the industrial source, the pollutant discharge amount of the domestic source, and the pollution of the non-point source. Emissions

Calculate the correlation between the pollutant discharge amount and the monitoring data through a preset algorithm, and obtain the pollutant discharge law portrait corresponding to the pollution source;

When it is detected that the concentration of pollutants in the target river section exceeds the standard, matching analysis is performed on the pollutants exceeding the standard according to the pollution discharge law profile to obtain the target pollution source;

When the target pollution source is the industrial source, query the target sewage outlet corresponding to the pollutant that exceeds the standard from the pollution discharge pattern portrait, and determine the target enterprise that has the illegal discharge behavior according to the preset unique identifier corresponding to the target sewage outlet , And send the preset warning information to the target terminal.
The big data-based pollution source locating device according to claim 9, wherein the big data-based pollution source locating device is executed by the processor to classify the multiple types of pollutants based on a preset classification model , The corresponding pollution source is obtained, and when the pollution source includes the steps of an industrial source, a living source, and a non-point source, the following steps are included:

Acquiring preset pollution data of the target enterprise, and determining the type of pollutant and the range of pollutant concentration from the preset pollution data to obtain a first pollutant data set;

Acquiring published pollutants in the target river section area, and setting the published pollutants as a second pollutant data set;

Fuse the first pollutant data set and the second pollutant data set, and identify the pollution source of the fused dye data set to obtain a pollutant sample library, and the pollution sources include industrial sources, domestic sources, and non-point sources;

Training the initial classification model according to the pollutant sample library to obtain a preset classification model;

Matching and identifying the multiple types of pollutants according to the preset classification model to obtain corresponding pollution sources.
The big data-based pollution source locating device according to claim 9, wherein the big data-based pollution source locating device is executed by the processor to calculate the pollutant emission amount corresponding to the pollution source according to the pollutant concentration When the pollutant discharge amount includes the pollutant discharge amount of the industrial source, the pollutant discharge amount of the domestic source, and the pollutant discharge amount of the non-point source, the steps include the following steps:

Obtain the waste water discharge volume, and perform multiplication calculation on the pollutant concentration and the waste water discharge volume to obtain the pollutant discharge volume of the industrial source;

Obtaining population distribution data, and performing multiplication operations based on the population distribution data and the pollutant concentration to obtain the pollution discharge amount of the living source;

Obtain rainfall data, and estimate the pollutant discharge amount of the non-point source according to the rainfall data. The rainfall data includes a preset runoff coefficient, a preset rainwater runoff, and the pollutant concentration.
The device for locating pollution sources based on big data according to claim 9, wherein the device for locating pollution sources based on big data is executed by the processor to calculate the pollutant emissions and the monitoring data through a preset algorithm The step of obtaining the pollutant discharge pattern image corresponding to the pollution source includes the following steps:

Serialize the pollutant discharge amount according to time sequence to obtain a time series sequence of the pollutant discharge amount;

Draw the time series emission law of the pollution source and calculate the correlation coefficient for the time series sequence of the multiple types of pollutants, the concentration of the pollutants and the discharge amount of the pollutants through a preset algorithm, and convert the correlation coefficients into weights, The preset algorithms include Pearson's correlation coefficient algorithm;

According to the time-series discharge rule of the pollution source, the plurality of sewage outlets and the target enterprise, a pollution discharge rule portrait is generated.
The device for locating pollution sources based on big data according to claim 12, wherein the device for locating pollution sources based on big data is executed by the processor by using a preset algorithm to detect the multiple types of pollutants, the The time series of the pollutant concentration and the pollutant discharge amount draws the time series emission law of the pollution source and calculates the correlation coefficient, and converts the correlation coefficient into a weight. When the preset algorithm includes the steps of the Pearson correlation coefficient algorithm, the following steps are included :

Draw the time series emission law of the pollutants for the time series of the multiple types of pollutants, the concentration of the pollutants, and the discharge amount of the pollutants through a preset algorithm, and the time series emission laws are the correlation scatter points picture;

Calculate the correlation coefficient between the pollutant emission amount and the pollutant concentration according to the time series emission rule by using the Pearson correlation coefficient algorithm;

Calculate the comprehensive pollutant discharge corresponding to the pollution source according to the pollutant discharge volume and the correlation coefficient to obtain the pollutant weight of the pollution source, and the pollutant weight is used to indicate the proportion of the pollutant discharge of the pollution source .
The device for locating pollution sources based on big data according to claim 13, wherein the device for locating pollution sources based on big data is executed by the processor. When querying the target sewage outlet corresponding to the pollutant that exceeds the standard, according to the preset unique identifier corresponding to the target sewage outlet, the step of determining the target enterprise that has sneaked discharge behavior and sending the preset warning information to the target terminal includes the following steps step:

When the pollution source corresponding to the pollutant is the industrial source, calculating the real-time emission amount according to the pollutant concentration and the pollutant weight;

Determining the standard discharge amount of the pollutant corresponding to the pollutant through the portrait of the pollution discharge law;

Judging whether the real-time emission amount is greater than the standard emission amount;

If the real-time emission amount is greater than the standard emission amount, the corresponding target sewage outlet is determined through the pollution emission law portrait, and the preset unique identifier corresponding to the target sewage outlet is obtained;

Query to obtain the target enterprise according to the preset unique identifier corresponding to the target sewage outlet;

Generate preset early warning information for the target enterprise, and send the preset early warning information to the target terminal.
The big data-based pollution source locating device according to any one of claims 9-14, wherein the big data-based pollution source locating device is executed by the processor when the target pollution source is the industrial source Query the target sewage outlet corresponding to the pollutant that exceeds the standard from the pollution discharge pattern portrait, determine the target company that has sneaked discharge according to the preset unique identifier corresponding to the target sewage outlet, and send the preset warning information to the target terminal After the steps, it also includes the following steps:

Obtain the water and electricity consumption data of the target company and the actual pollutant discharge volume of the target company;

The theoretical amount of pollutants produced is calculated according to the water and electricity data and the pollution production coefficient of the target enterprise, and the pollution production coefficient is used to indicate that it is determined in advance based on the target enterprise's preset production facilities and preset materials ；

Multiplying the amount of pollutants produced by a preset emission coefficient to obtain a theoretical pollutant emission amount, where the preset emission coefficient is a data interval obtained by pre-calculation based on preset mass data;

When it is determined through the conservation of materials that the amount of pollutants produced is less than the actual discharge amount of the target company or the theoretical discharge amount of the pollutants is greater than the actual discharge amount of the pollutants, it is determined that the target company has data fraud;

When it is determined that the actual emission volume of the target company is abnormal discharge or the actual emission volume of the target company is greater than the theoretical discharge volume of pollutants according to the pollution discharge law profile, it is determined that the target company has an illegal discharge behavior.
A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following method for locating pollution sources based on big data is implemented:

Monitor the target river section within the preset time period and collect data to obtain monitoring data corresponding to multiple sewage outlets. The monitoring data includes multiple types of pollutants and pollutant concentrations. Each sewage outlet passes the preset The unique identifier is associated with the target company;

Classify the multiple types of pollutants based on a preset classification model to obtain corresponding pollution sources, where the pollution sources include industrial sources, living sources, and non-point sources;

Calculate the pollutant discharge amount corresponding to the pollution source according to the pollutant concentration. The pollutant discharge amount includes the pollutant discharge amount of the industrial source, the pollutant discharge amount of the domestic source, and the pollution of the non-point source. Emissions

Calculate the correlation between the pollutant discharge amount and the monitoring data through a preset algorithm, and obtain the pollutant discharge law portrait corresponding to the pollution source;

When it is detected that the concentration of pollutants in the target river section exceeds the standard, matching analysis is performed on the pollutants exceeding the standard according to the pollution discharge law portrait to obtain the target pollution source;

When the target pollution source is the industrial source, query the target sewage outlet corresponding to the pollutant that exceeds the standard from the pollution discharge pattern portrait, and determine the target enterprise that has the illegal discharge behavior according to the preset unique identifier corresponding to the target sewage outlet , And send the preset warning information to the target terminal.
The computer-readable storage medium according to claim 16, wherein the program for locating pollution sources based on big data is executed by the processor to classify the multiple types of pollutants based on a preset classification model to obtain corresponding pollution sources When the pollution source includes the steps of industrial source, domestic source and non-point source, the following steps are included:

Acquiring preset pollution data of the target enterprise, and determining the type of pollutant and the range of pollutant concentration from the preset pollution data to obtain a first pollutant data set;

Acquiring published pollutants in the target river section area, and setting the published pollutants as a second pollutant data set;

Fuse the first pollutant data set and the second pollutant data set, and identify the pollution source of the fused dye data set to obtain a pollutant sample library. The pollution sources include industrial sources, domestic sources, and non-point sources;

Training the initial classification model according to the pollutant sample library to obtain a preset classification model;

Matching and identifying the multiple types of pollutants according to the preset classification model to obtain corresponding pollution sources.
The computer-readable storage medium according to claim 16, wherein the program for locating the pollution source based on big data is executed by a processor to calculate the pollutant emission amount corresponding to the pollution source according to the pollutant concentration, and the pollutant When the discharge amount includes the steps of the pollutant discharge amount of the industrial source, the pollutant discharge amount of the domestic source and the pollutant discharge amount of the non-point source, the following steps are included:

Obtain the waste water discharge volume, and perform multiplication calculation on the pollutant concentration and the waste water discharge volume to obtain the pollutant discharge volume of the industrial source;

Obtaining population distribution data, and performing multiplication operations based on the population distribution data and the pollutant concentration to obtain the pollution discharge amount of the living source;

Obtain rainfall data, and estimate the pollutant discharge amount of the non-point source according to the rainfall data. The rainfall data includes a preset runoff coefficient, a preset rainwater runoff, and the pollutant concentration.
The computer-readable storage medium according to claim 16, wherein the program for locating pollution sources based on big data is executed by a processor, and the correlation between the pollutant emission amount and the monitoring data is calculated through a preset algorithm to obtain The steps of the pollution source corresponding to the pollution discharge law portrait include the following steps:

Serialize the pollutant discharge amount according to time sequence to obtain a time series sequence of the pollutant discharge amount;

Draw the time series emission law of the pollution source and calculate the correlation coefficient for the time series sequence of the multiple types of pollutants, the concentration of the pollutant and the discharge amount of the pollutant through a preset algorithm, and convert the correlation coefficient into a weight, The preset algorithms include Pearson's correlation coefficient algorithm;

According to the time-series discharge rule of the pollution source, the plurality of sewage outlets and the target enterprise, a pollution discharge rule portrait is generated.
According to the computer-readable storage medium of claim 19, the program for locating pollution sources based on big data is executed by a processor, and the processing of the plurality of types of pollutants, the concentration of pollutants, and the concentration of pollutants by a preset algorithm is executed by a processor. The time series sequence of the pollutant emissions draws the time series emission law of the pollution source and calculates the correlation coefficient, and converts the correlation coefficient into a weight. When the preset algorithm includes the steps of the Pearson correlation coefficient algorithm, the following steps are included:

Draw the time series emission law of the pollutants for the time series of the multiple types of pollutants, the concentration of the pollutants, and the discharge amount of the pollutants through a preset algorithm, and the time series emission laws are the correlation scatter points picture;

Calculate the correlation coefficient between the pollutant emission amount and the pollutant concentration according to the time series emission rule by using the Pearson correlation coefficient algorithm;

Calculate the comprehensive pollutant discharge corresponding to the pollution source according to the pollutant discharge volume and the correlation coefficient to obtain the pollutant weight of the pollution source, and the pollutant weight is used to indicate the proportion of the pollutant discharge of the pollution source .