CN117094473A

CN117094473A - Environment-friendly data acquisition and monitoring control method and system based on industrial Internet of things

Info

Publication number: CN117094473A
Application number: CN202311340348.0A
Authority: CN
Inventors: 谷辉宁; 邵国义; 谢海星; 何晓创
Original assignee: Jiangsu Akman Environmental Protection Technology Co ltd
Current assignee: Jiangsu Akman Environmental Protection Technology Co ltd
Priority date: 2023-10-17
Filing date: 2023-10-17
Publication date: 2023-11-21
Anticipated expiration: 2043-10-17
Also published as: CN117094473B

Abstract

The invention relates to the technical field of water pollution monitoring, and discloses an environmental protection data acquisition and monitoring control method and system based on industrial Internet of things, wherein the method comprises the steps of acquiring first water quality pollution monitoring data of a monitoring well and extracting second water quality pollution monitoring data; q pollution wells are determined according to the first water quality pollution monitoring data and the second water quality pollution monitoring data, and the largest cross pollution monitoring well is determined; acquiring an industrial park map, marking cross contamination monitoring wells and pollution wells in the map, and screening L suspected pollution lines based on the marked industrial park map; determining the enterprises in the peripheral gardens of each suspected pollution line, extracting the production information of the enterprises in the peripheral gardens, analyzing the production information of the enterprises in the peripheral gardens, and determining S suspected illegal arrangement enterprises; calculating the slope coefficient of each suspected pollution line, determining a pollution line according to the slope coefficient, and determining a stealth enterprise on the pollution line.

Description

Environment-friendly data acquisition and monitoring control method and system based on industrial Internet of things

Technical Field

The invention relates to the technical field of water pollution monitoring, in particular to an environment-friendly data acquisition and monitoring control method and system based on industrial Internet of things.

Background

The industrial park is taken as an important component of modern industrialization, and provides a key support for the rapid development of economy and the continuous progress of society; however, with the continuous expansion and deepening of industrial activities, the problem of groundwater pollution is gradually highlighted, and becomes one of environmental challenges to be solved urgently; groundwater is used as an important water source supply source and an environmental ecological element, and pollution of the groundwater not only threatens human health, but also can cause ecological system damage to influence realization of sustainable development targets; at present, the formation mechanism of groundwater pollution in an industrial park is complex and various, common pollution sources comprise industrial wastewater, production emission, chemical storage and the like, and the pollution condition of the groundwater is characterized by complex cause, strong concealment and difficult reversion, so that the pollutants diffuse through groundwater flow, the gradual deterioration of the groundwater quality is possibly caused, and potential hazard is caused to surrounding areas; therefore, how to perform data collection and monitoring control on underground pollution of an industrial park is a problem to be solved.

At present, the environmental protection data collection and monitoring control method of groundwater is mainly realized based on a mode of manually collecting pollutant discharge data, and of course, there is a partially automatic mode, for example, the China patent with the authority of publication No. CN114529226B discloses a monitoring method and system of groundwater pollution based on industrial Internet of things, for example, the China patent with the application publication No. CN115660438A discloses a soil and groundwater pollution early warning grade evaluation method of industrial park, although the method can monitor and evaluate the pollution degree of groundwater, the inventor researches and practical application find that the method and the prior art have at least the following defects:

(1) The method is lack of effective data analysis and excavation, and cannot acquire a pollution line of the underground water in an industrial park, so that effective data support and decision basis cannot be provided for the underground water restoration;

(2) Only aiming at single pollution condition, the method is difficult to be practically applied to the pollution control and prevention and control of the underground water of the industrial park in complex condition, and the cross pollution condition of the underground water of the industrial park cannot be known, and a plurality of pollution thieves in the industrial park cannot be traced to the source based on the cross pollution condition.

Disclosure of Invention

In order to overcome the defects in the prior art, the embodiment of the invention provides an environment-friendly data acquisition and monitoring control method and system based on the industrial Internet of things.

In order to achieve the above purpose, the present invention provides the following technical solutions:

an environmental protection data acquisition and monitoring control method based on industrial Internet of things, the method comprising:

acquiring first water quality pollution monitoring data of M monitoring wells at the moment T, and extracting second water quality pollution monitoring data of each monitoring well at the moment T-N; the first water quality pollution monitoring data comprises R water quality element sets EA, the second water quality pollution monitoring data comprises R water quality element sets EB, and M, N, R are all integer sets larger than zero;

Determining Q pollution wells according to the first water quality pollution monitoring data and the second water quality pollution monitoring data, and determining a maximum cross pollution monitoring well, wherein Q is a positive integer set greater than zero;

acquiring an industrial park map, marking cross contamination monitoring wells and pollution wells in the industrial park map, screening L suspected pollution lines based on the marked industrial park map, wherein L is a positive integer set larger than zero;

determining the enterprises in the peripheral circles of each suspected pollution line, extracting the production information of the enterprises in each peripheral circle, and analyzing based on the production information of the enterprises in each peripheral circle to determine S suspected illegal enterprises; the production information comprises an element set of a product raw material and an element set of industrial waste;

calculating a slope coefficient of each suspected pollution line, determining a pollution line according to the slope coefficient, and determining at least one stealth enterprise on the pollution line.

Further, determining Q contaminated wells includes:

extracting the element number of each water quality element set EA in the first water quality pollution monitoring data;

and comparing the element number of each water quality element set EA with a preset element number threshold, if the element number of the water quality element set EA is smaller than or equal to the preset element number threshold, judging the corresponding monitoring well as an uncontaminated monitoring well, if the element number of the water quality element set EA is larger than the preset element number threshold, judging the corresponding monitoring well as a polluted monitoring well, and counting the number of the polluted monitoring wells, so that Q polluted wells are obtained.

Further, determining a maximum cross-contamination monitoring well, comprising:

extracting R water quality element sets EA in the first water quality pollution monitoring data and R water quality element sets EB in the second water quality pollution monitoring data;

calculating the difference value of each water quality element set EA and the corresponding water quality element set EB based on a preset set relation, marking the difference value of each water quality element set EA and the corresponding water quality element set EB as set element differences, and obtaining U set element differences, wherein U is a positive integer set larger than zero;

sorting the U set element differences according to the difference from large to small, and extracting a first set element difference;

and extracting a corresponding water quality element set EA or a water quality element set EB based on the ordered first aggregate element difference, and determining a corresponding monitoring well as a maximum cross contamination monitoring well according to the corresponding water quality element set EA or the water quality element set EB.

Further, determining a maximum cross-contamination monitoring well, further comprising:

acquiring water quality element sets EA of Q polluted wells, and acquiring the element number in each water quality element set EA of the polluted wells;

sequencing the water quality element set EA of each pollution well from large to small according to the number of elements, and extracting and sequencing a first water quality element set EA;

And acquiring the corresponding pollution well according to the ordered first water quality element set EA, and determining the corresponding pollution well as the largest cross-contamination monitoring well.

Further, the screening logic of the L suspected pollution lines is specifically as follows:

a. acquiring coordinates of the largest cross-contamination monitoring well in the industrial park map, and determining the furthest contamination monitoring well in the industrial park map;

b. searching according to a preset searching distance condition based on the coordinates of the largest cross-contamination monitoring well to obtain an adjacent contamination monitoring well of the largest cross-contamination monitoring well;

c. in an industrial park map, connecting the largest cross-contamination monitoring well and adjacent contamination monitoring wells of the largest cross-contamination monitoring well in a line manner;

d. taking the adjacent pollution monitoring well as a target pollution monitoring well, searching the adjacent pollution monitoring well of the target pollution monitoring well by taking the target pollution monitoring well as a reference, and connecting the target pollution monitoring well with the adjacent pollution monitoring well of the target pollution monitoring well in a line way;

e. repeating the step d until the adjacent pollution monitoring well of the target pollution monitoring well is the farthest pollution monitoring well, and connecting the target pollution monitoring well with the farthest pollution monitoring well in a line way to obtain L suspected pollution lines.

Further, the logic for determining the furthest contamination monitoring well is as follows:

extracting the coordinates of the largest cross-contamination monitoring well, and dividing Q contamination wells based on the largest cross-contamination monitoring well to obtain K-directional contamination wells and coordinates of the contamination wells;

calculating the distance between the largest cross contamination monitoring well and the pollution well in each direction based on the pollution well coordinates in each direction and the largest cross contamination monitoring well coordinates, and taking the distance between the largest cross contamination monitoring well and the pollution well in each direction as a position distance to obtain D position distances in K directions;

and sequencing the D position intervals in each direction according to the numerical value, acquiring the pollution wells of the first position interval sequenced in each direction, and taking the pollution wells of the first position interval sequenced in each direction as the farthest pollution monitoring wells in each direction.

Further, analyzing based on the production information of the enterprises in each peripheral garden, including:

respectively carrying out similarity calculation on the water quality element set EA and an element set of a product raw material and an element set of industrial waste in production information so as to obtain first similarity and second similarity;

averaging the first similarity and the second similarity to obtain a similarity average;

And comparing the similarity mean value with a preset similarity mean value threshold value, if the similarity mean value is larger than the preset similarity mean value threshold value, judging that the enterprise in the corresponding peripheral garden is a suspected stealth enterprise, and if the similarity mean value is smaller than or equal to the preset similarity mean value threshold value, judging that the enterprise in the corresponding peripheral garden is a non-suspected stealth enterprise.

Further, calculating a slope coefficient of each suspected contaminated line includes:

acquiring a water quality element set EA of a pollution well in each suspected pollution line, and extracting the element number of each water quality element set EA;

marking each pollution well in the suspected pollution line according to the direction from the largest cross pollution monitoring well to the farthest pollution monitoring well;

constructing a quantity line graph by taking the label of each pollution well as a horizontal axis and the quantity of elements corresponding to each pollution well as a vertical axis, and taking data points corresponding to each horizontal axis and the vertical axis as nodes;

and calculating the slope of each two adjacent nodes, taking the slope of each two adjacent nodes as a slope coefficient to obtain H slope coefficients, wherein H is a positive integer set larger than zero.

Further, determining a contaminated route from the slope coefficient includes: comparing each slope coefficient with a preset slope coefficient threshold; if the slope coefficient is greater than or equal to a preset slope coefficient threshold value, judging that the corresponding suspected pollution line is a pollution line; if the slope coefficient is smaller than the preset slope coefficient threshold value, judging that the corresponding suspected pollution line is a non-pollution line.

Further, determining at least one theft-and-rank enterprise, comprising:

acquiring the polluted soil information of the periphery of each suspected secretly-arranged enterprise on a polluted line; the polluted soil information comprises the quantity of polluted elements, the types of the polluted elements, the area of a polluted area and the soil pollution depth;

obtaining the pollution concentration of a pollution well with the minimum distance in a pollution line;

carrying out correlation calculation on the polluted soil information and the pollution concentration by using the Pearson correlation coefficient to obtain a correlation coefficient;

comparing the correlation coefficient with a correlation coefficient threshold, and if the correlation coefficient is larger than the correlation coefficient threshold, judging that the corresponding suspected stealth enterprise is a stealth enterprise; and if the correlation coefficient is smaller than or equal to the correlation coefficient threshold, judging that the corresponding suspected stealth enterprise is a non-stealth enterprise.

An environmental protection data acquisition and monitoring control system based on industry thing networking includes:

the data acquisition module is used for acquiring first water quality pollution monitoring data of M monitoring wells at the moment T and extracting second water quality pollution monitoring data of each monitoring well at the moment T-N; the first water quality pollution monitoring data comprises R water quality element sets EA, the second water quality pollution monitoring data comprises R water quality element sets EB, and M, N, R are all integer sets larger than zero;

The pollution determining module is used for determining Q pollution wells according to the first water quality pollution monitoring data and the second water quality pollution monitoring data, and determining a maximum cross pollution monitoring well, wherein Q is a positive integer set greater than zero;

the pollution line screening module is used for acquiring an industrial park map, marking cross pollution monitoring wells and pollution wells in the industrial park map, screening L suspected pollution lines based on the marked industrial park map, wherein L is a positive integer set larger than zero;

the pollution enterprise investigation module is used for determining the enterprises in the peripheral gardens of each suspected pollution line, extracting the production information of the enterprises in each peripheral garden, and analyzing based on the production information of the enterprises in each peripheral garden to determine S suspected illegal enterprises; the production information comprises an element set of a product raw material and an element set of industrial waste;

the pollution tracing module is used for calculating the slope coefficient of each suspected pollution line, determining the pollution line according to the slope coefficient and determining at least one stealth enterprise on the pollution line.

An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing an environmental protection data collection and monitoring control method based on industrial internet of things as described in any one of the above when executing the computer program.

A computer readable storage medium having a computer program stored thereon, the computer program when executed by a processor implementing any one of the above methods for environmental protection data collection and monitoring control based on industrial internet of things.

Compared with the prior art, the application has the beneficial effects that:

1. the application discloses an environmental protection data acquisition and monitoring control method and system based on industrial Internet of things, which comprises the steps of firstly acquiring first water quality pollution monitoring data of a monitoring well and extracting second water quality pollution monitoring data; then, determining Q pollution wells according to the first water quality pollution monitoring data and the second water quality pollution monitoring data, and determining the largest cross pollution monitoring well; then, an industrial park map is obtained, cross pollution monitoring wells and pollution wells are marked in the map, and L suspected pollution lines are screened out based on the marked industrial park map; determining the enterprises in the peripheral gardens of each suspected pollution line, extracting the production information of the enterprises in the peripheral gardens, analyzing the production information of the enterprises in the peripheral gardens, and determining S suspected illegal arrangement enterprises; finally calculating the slope coefficient of each suspected pollution line, determining a pollution line according to the slope coefficient, and determining a stealth and drainage enterprise on the pollution line; through the steps, the method can acquire the pollution line of the underground water in the industrial park, thereby being beneficial to providing effective data support and decision basis for the underground water restoration.

2. The application discloses an environment-friendly data acquisition and monitoring control method and system based on an industrial Internet of things, which can learn the cross contamination condition of underground water of an industrial park by determining a stealth enterprise on the basis of determining a pollution route, thereby being beneficial to tracing a plurality of pollution stealth enterprises in the industrial park and further being beneficial to the pollution control and prevention and control of the underground water of the industrial park in actual application to complex situations.

Drawings

FIG. 1 is a schematic diagram of an environmental protection data acquisition and monitoring control method based on industrial Internet of things provided by the application;

FIG. 2 is a schematic diagram of an environmental protection data acquisition and monitoring control system based on the industrial Internet of things provided by the application;

FIG. 3 is a annotated industrial park map provided by the present application;

FIG. 4 is a quantitative plot of the present application;

fig. 5 is a schematic structural diagram of an electronic device according to the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Example 1

Referring to fig. 2, the disclosure of the present embodiment provides an environmental protection data acquisition and monitoring control system based on industrial internet of things, including:

the data acquisition module 210 is configured to acquire first water quality pollution monitoring data of M monitoring wells at time T, and extract second water quality pollution monitoring data of each monitoring well at time T-N; the first water quality pollution monitoring data comprises R water quality element sets EA, the second water quality pollution monitoring data comprises R water quality element sets EB, and M, N, R are all integer sets larger than zero;

it should be appreciated that: the M monitoring wells in the industrial park are preset according to the underground water distribution range of the industrial park, and each monitoring well is distributed in the industrial park according to preset intervals, such as 30M intervals; a water quality in-situ detection device is preset in each monitoring well based on the industrial Internet of things technology, a plurality of sensors are arranged on the water quality in-situ detection device, the sensors comprise but are not limited to VOCs sensors, persistent Organic Pollutants (POPs) sensors, heavy metal sensors, chloride sensors, radioactive substance sensors, pH value sensors and the like, the types and the number of the sensors of the water quality in-situ detection device can be manually determined according to the pollution condition of an industrial park, and the water quality in-situ detection device is not excessively limited;

It should be noted that: T-N is the historical moment, the value of N can be determined manually, and the method is not limited excessively; in addition, it should be appreciated that: the second water quality pollution monitoring data are pre-stored in a system database, the specific number of water quality element sets in the first water quality pollution monitoring data and the second water quality pollution monitoring data is determined according to the number of monitoring wells, and each water quality element set contains a plurality of pollution elements (such as lead, cadmium, chromium, mercury and the like); wherein each water quality element set in the first water quality pollution monitoring data is as followsEach water quality element set in the second water quality pollution monitoring data is +.>， />Is an integer set greater than zero;

and also needsThe following are to be described: the water quality element set EA in the first water quality pollution monitoring data and the water quality element set EB in the second water quality pollution monitoring data are mutually bound based on a preset set relation, and further description is that one water quality element set EA in the first water quality pollution monitoring data corresponds to one water quality element set EB in the second water quality pollution monitoring data one by one; the following are illustrated: assume that there are three monitoring wells, respectively、/>And->When the first water pollution monitoring data of each monitoring well under the T moment is acquired, the water pollution monitoring data is in existence +. >、/>And->The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Correspond to->，/>Correspond to->，/>Correspond to->The method comprises the steps of carrying out a first treatment on the surface of the At this time, when the second water pollution monitoring data of each monitoring well at the time of T-N is extracted, there are/>、And->Three water quality element sets correspond to the water quality element sets, namelyCorresponding to->，/>Corresponding to->，/>Corresponding to->But it should be noted that->Not necessarily equal to->，/>Not necessarily equal to->，/>Not necessarily equal to->This is due to the difference in data acquisition time and contamination source, resulting in corresponding monitoring wells in the fieldThe water quality elements and the quantity acquired at different times may be different;

a pollution determination module 220, configured to determine Q pollution wells according to the first water quality pollution monitoring data and the second water quality pollution monitoring data, and determine a maximum cross-contamination monitoring well, where Q is a positive integer set greater than zero;

in practice, determining Q contaminated wells includes:

comparing the element number of each water quality element set EA with a preset element number threshold, if the element number of the water quality element set EA is smaller than or equal to the preset element number threshold, judging the corresponding monitoring well as an uncontaminated monitoring well, if the element number of the water quality element set EA is larger than the preset element number threshold, judging the corresponding monitoring well as a polluted monitoring well, and counting the number of the polluted monitoring wells, so as to obtain Q polluted wells;

In one embodiment, determining a maximum cross-contamination monitoring well comprises:

the description is as follows: for the explanation of the preset set relationship, the details refer to the above description, and the detailed description is not repeated here;

extracting a corresponding water quality element set EA or a water quality element set EB based on the ordered first aggregate element difference, and determining a corresponding monitoring well as a maximum cross contamination monitoring well according to the corresponding water quality element set EA or the water quality element set EB;

it should be noted that: based on the above description, the aggregate element difference is obtained by performing a difference calculation based on the water quality element set EA and the water quality element set EB, so that according to the ordered first aggregate element difference, a corresponding water quality element set EA or water quality element set EB is obtained, and further, because the water quality element set EA or water quality element set EB is obtained through the same monitoring well, a corresponding associated monitoring well can be obtained according to the water quality element set EA or water quality element set EB;

It should be appreciated that: sequencing the monitoring wells corresponding to the first aggregate element differences to be the monitoring wells with the largest cross contamination;

in another embodiment, determining a maximum cross-contamination monitoring well further comprises:

acquiring corresponding pollution wells according to the ordered first water quality element set EA, and determining the corresponding pollution wells as the largest cross-contamination monitoring wells;

it should be appreciated that: sequencing the pollution wells corresponding to the first water quality element set EA as the largest cross-pollution monitoring wells;

the pollution line screening module 230 is configured to obtain an industrial park map, label cross-contamination monitoring wells and pollution wells in the industrial park map, screen L suspected pollution lines based on the labeled industrial park map, and L is a positive integer set greater than zero;

it should be noted that: the industrial park map is pre-stored in a system database, and the industrial park map is marked with a plurality of monitoring wells and the locations of enterprises in the park; also to be described is: after the industrial park map is marked, the positions of a plurality of pollution monitoring wells and a maximum cross-pollution monitoring well in the industrial park map can be obtained (as shown in figure 3 (marked industrial park map));

In implementation, the screening logic of the L suspected contaminated lines is specifically as follows:

specifically, the logic for determining the furthest contamination monitoring well is as follows:

it should be noted that: the logic for partitioning the Q contaminated wells is: establishing a direction coordinate system by taking the maximum cross contamination monitoring well as a reference, and dividing the contamination well falling into each direction interval into corresponding directions;

the following are illustrated: as shown in fig. 3 (labeled industrial park map), the first quadrant, the second quadrant, the third quadrant and the fourth quadrant are included, each quadrant corresponds to a direction, namely a northeast direction, a northwest direction, a southwest direction and a southeast direction, and if the pollution well falls into the first quadrant, all the pollution wells in the quadrant are divided into the northeast direction; similarly, the same is true for the division of the northwest direction, the southwest direction and the southeast direction, and redundant description is not made here;

sequencing the D position intervals in each direction according to the numerical value, acquiring pollution wells of which the first position intervals are sequenced in each direction, and taking the pollution wells of which the first position intervals are sequenced in each direction as the farthest pollution monitoring wells in each direction;

the above embodiments are described by way of example: as shown in fig. 3 (labeled industrial park map), A0 is the largest cross contamination monitoring well, and four furthest contamination monitoring wells A1, A2, A3 and A4 are obtained by constructing a direction coordinate system for A0 and then calculating; it should be noted that: the position distance is calculated based on a two-point distance formula;

it should be noted that: the preset search distance condition refers to a preset search radius, which is determined according to experimental personnel; further exemplary, if the preset search distance condition is preset to be 5 meters, when the largest cross-contamination monitoring well is taken as a reference center, if the largest cross-contamination monitoring well exists with a radius of 5 meters, the largest cross-contamination monitoring well is taken as an adjacent contamination monitoring well of the largest cross-contamination monitoring well;

it should be noted that: searching the logic of the adjacent pollution monitoring well of the target pollution monitoring well and the adjacent pollution monitoring well for acquiring the largest cross pollution monitoring well, which are not repeated;

e. repeating the step d until the adjacent pollution monitoring well of the target pollution monitoring well is the farthest pollution monitoring well, and connecting the target pollution monitoring well with the farthest pollution monitoring well in a line way to obtain L suspected pollution lines;

as shown in fig. 3 (labeled industrial park map), when the adjacent pollution monitoring wells are repeatedly acquired according to the logic until the adjacent pollution monitoring well of the target pollution monitoring well is the farthest pollution monitoring well, four suspected pollution lines L1, L2, L3 and L4 are obtained;

the contaminated enterprise checking module 240 is configured to determine an enterprise in a peripheral garden of each suspected contaminated line, extract production information of the enterprise in each peripheral garden, and analyze based on the production information of the enterprise in each peripheral garden to determine S suspected secretly-arranged enterprises; the production information comprises an element set of a product raw material and an element set of industrial waste;

It should be noted that: the logic for determining the enterprise in the peripheral garden of each suspected contaminated line is as follows: obtaining the distance from each in-garden enterprise to the suspected pollution line, taking the distance from each in-garden enterprise to the suspected pollution line as a line distance, comparing the line distance with a preset line distance threshold, judging that the corresponding in-garden enterprise is the peripheral in-garden enterprise of the suspected pollution line if the line distance is greater than or equal to the preset line distance threshold, and conversely, judging that the corresponding in-garden enterprise is the peripheral in-garden enterprise of the non-suspected pollution line if the line distance is less than the preset line distance threshold;

also to be described is: the production information of enterprises in each peripheral garden is obtained by manual investigation in advance and is prestored in a system database; and re-acquiring every other preset time period and updating the acquired data into a system database;

specifically, the analysis based on the production information of the enterprises in each peripheral garden includes:

Comparing the similarity mean value with a preset similarity mean value threshold value, if the similarity mean value is larger than the preset similarity mean value threshold value, judging that the enterprise in the corresponding peripheral garden is a suspected stealth enterprise, and if the similarity mean value is smaller than or equal to the preset similarity mean value threshold value, judging that the enterprise in the corresponding peripheral garden is a non-suspected stealth enterprise;

it should be noted that: the similarity calculation adopts a cosine similarity algorithm, and is further explained as follows: performing similarity calculation on the water quality element set EA and an element set of a product raw material in production information to obtain first similarity, performing similarity calculation on the water quality element set EA and an element set of industrial waste to obtain second similarity, averaging the first similarity and the second similarity to obtain a similarity average value, and comparing the similarity average values to determine S suspected illegal arrangement enterprises; in short, a suspected thief-proof enterprise is determined by analyzing production information and groundwater pollution information of the enterprise;

the pollution tracing module 250 is configured to calculate a slope coefficient of each suspected pollution line, determine a pollution line according to the slope coefficient, and determine at least one stealth enterprise on the pollution line;

Specifically, calculating the slope coefficient of each suspected pollution line includes:

calculating the slope of each two adjacent nodes, taking the slope of each two adjacent nodes as a slope coefficient to obtain H slope coefficients, wherein H is a positive integer set larger than zero;

specifically, determining the pollution route according to the slope coefficient includes: comparing each slope coefficient with a preset slope coefficient threshold; if the slope coefficient is greater than or equal to a preset slope coefficient threshold value, judging that the corresponding suspected pollution line is a pollution line; if the slope coefficient is smaller than the preset slope coefficient threshold value, judging that the corresponding suspected pollution line is a non-pollution line;

the above is exemplified by: as shown in fig. 4 (a is a number line chart), in fig. 4, where (a) is a distribution of element numbers of one suspected pollution line, points (1, 7) represent the maximum cross-contamination monitoring well (reference numeral 1) with element numbers of 7, points (2, 6) represent the contamination well (reference numeral 2) with element numbers of 6, points (3, 5) represent the contamination well (reference numeral 3) with element numbers of 5, … …, points (6, 2) represent the contamination well (reference numeral 6) with element numbers of 2, assuming that the preset slope coefficient threshold is zero, the suspected pollution line corresponding to (a) in fig. 4 is determined to be a polluted line by calculating the slope coefficient of the suspected pollution line in the case of (a) in fig. 4, and conversely, referring to (b) in fig. 4 with element numbers of another suspected pollution line, if the slope coefficient corresponding to (b) in fig. 4 is calculated, the preset slope coefficient threshold is determined to be a non-polluted line according to the slope coefficient threshold of zero;

Specifically, determining at least one theft-and-rank enterprise includes:

it should be noted that: the smallest-distance pollution well in the pollution line refers to the pollution well which is in the pollution line and is the smallest distance to suspected thieves; the concentration of the contaminant is collected based on a concentration sensor, including but not limited to one of an electrochemical sensor, an optical sensor, or an ion selective electrode sensor;

specifically, the calculation formula of the pearson correlation coefficient is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein: r is the pearson correlation coefficient; />Represents vectorised pollution concentration, < >>Is->Is the average value of (2); />Represents vectorized pollutant element number, pollutant element type, pollutant area and soil pollutant depth, ++>Is->Mean value of->Representing the number of data;

comparing the correlation coefficient with a correlation coefficient threshold, and if the correlation coefficient is larger than the correlation coefficient threshold, judging that the corresponding suspected stealth enterprise is a stealth enterprise; if the correlation coefficient is smaller than or equal to the correlation coefficient threshold, judging that the corresponding suspected stealth enterprise is a non-stealth enterprise;

The underground water pollution tracing method and the underground water pollution tracing system can be used for determining the stealing and discharging enterprises on the basis of determining the pollution route, and are beneficial to timely pollution repair, pollution tracing and pollution control by learning the pollution route and the pollution enterprises.

Example 2

Referring to fig. 1, the disclosure of the present embodiment provides an environmental protection data acquisition and monitoring control method based on industrial internet of things, which includes:

s101: acquiring first water quality pollution monitoring data of M monitoring wells at the moment T, and extracting second water quality pollution monitoring data of each monitoring well at the moment T-N; the first water quality pollution monitoring data comprises R water quality element sets EA, the second water quality pollution monitoring data comprises R water quality element sets EB, and M, N, R are all integer sets larger than zero;

also to be described is: the water quality element set EA in the first water quality pollution monitoring data and the water quality element set EB in the second water quality pollution monitoring data are mutually bound based on a preset set relation, and further description is that one water quality element set EA in the first water quality pollution monitoring data corresponds to one water quality element set EB in the second water quality pollution monitoring data one by one; the following are illustrated: assume that there are three monitoring wells, respectively、/>And->When the first water pollution monitoring data of each monitoring well under the T moment is acquired, the water pollution monitoring data is in existence +. >、/>And->The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Correspond to->，/>Correspond to->，/>Correspond to->The method comprises the steps of carrying out a first treatment on the surface of the At this time, there is +.A second water pollution monitoring data of each monitoring well at the time of T-N is extracted>、And->Three water quality element sets correspond to the water quality element sets, namelyCorresponding to->，/>Corresponding to->，/>Corresponding to->But it should be noted that->Not necessarily equal to->，/>Not necessarily equal to->，/>Not necessarily equal to->This is due to the difference in data acquisition time and pollution sources, which results in the possible difference in the quality elements and quantity acquired by the corresponding monitoring wells at different times;

s102: determining Q pollution wells according to the first water quality pollution monitoring data and the second water quality pollution monitoring data, and determining a maximum cross pollution monitoring well, wherein Q is a positive integer set greater than zero;

in practice, determining Q contaminated wells includes:

s103: acquiring an industrial park map, marking cross contamination monitoring wells and pollution wells in the industrial park map, screening L suspected pollution lines based on the marked industrial park map, wherein L is a positive integer set larger than zero;

s104: determining the enterprises in the peripheral circles of each suspected pollution line, extracting the production information of the enterprises in each peripheral circle, and analyzing based on the production information of the enterprises in each peripheral circle to determine S suspected illegal enterprises; the production information comprises an element set of a product raw material and an element set of industrial waste;

s105: calculating a slope coefficient of each suspected pollution line, determining a pollution line according to the slope coefficient, and determining at least one stealth enterprise on the pollution line;

Specifically, determining at least one theft-and-rank enterprise includes:

Example 3

Referring to fig. 5, the disclosure provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements any one of the environmental protection data collection and monitoring control method based on the industrial internet of things provided by the above methods when executing the computer program.

Since the electronic device described in this embodiment is an electronic device for implementing the environmental protection data collection and monitoring control method based on the industrial internet of things in the embodiment of the present application, based on the environmental protection data collection and monitoring control method based on the industrial internet of things described in the embodiment of the present application, a person skilled in the art can understand a specific implementation manner of the electronic device and various variations thereof, so that a detailed description of how the electronic device is implemented in the embodiment of the present application is omitted herein. As long as the person skilled in the art implements the electronic device adopted by the environmental protection data acquisition and monitoring control method based on the industrial Internet of things in the embodiment of the application, the electronic device belongs to the scope of protection intended by the application.

Example 4

The embodiment discloses a computer readable storage medium, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the environmental protection data acquisition and monitoring control method based on the industrial Internet of things provided by any one of the methods when executing the computer program.

The above formulas are all formulas with dimensionality removed and numerical value calculated, the formulas are formulas with the latest real situation obtained by software simulation by collecting a large amount of data, and preset parameters, weights and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center over a wired network or a wireless network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims

1. An environmental protection data acquisition and monitoring control method based on industrial Internet of things is characterized by comprising the following steps:

2. The method for environmental protection data collection and monitoring control based on industrial internet of things of claim 1, wherein determining Q contaminated wells comprises:

3. The method for environmental protection data collection and monitoring control based on industrial internet of things of claim 2, wherein determining a maximum cross-contamination monitoring well comprises:

4. The method for environmental protection data collection and monitoring control based on industrial internet of things of claim 3, wherein determining a maximum cross-contamination monitoring well further comprises:

5. The method for environmental protection data collection and monitoring control based on industrial internet of things according to claim 4, wherein the screening logic of the L suspected pollution lines is as follows:

6. The environmental protection data collection and monitoring control method based on the industrial internet of things according to claim 5, wherein the determination logic of the furthest pollution monitoring well is as follows:

7. The method for environmental protection data collection and monitoring control based on industrial internet of things of claim 6, wherein analyzing based on production information of enterprises in each peripheral garden comprises:

8. The method for environmental protection data collection and monitoring control based on industrial internet of things of claim 7, wherein calculating the slope coefficient of each suspected pollution line comprises:

9. The method for environmental protection data collection and monitoring control based on industrial internet of things of claim 8, wherein determining the pollution route according to the slope coefficient comprises: comparing each slope coefficient with a preset slope coefficient threshold; if the slope coefficient is greater than or equal to a preset slope coefficient threshold value, judging that the corresponding suspected pollution line is a pollution line; if the slope coefficient is smaller than the preset slope coefficient threshold value, judging that the corresponding suspected pollution line is a non-pollution line.

10. The method for environmental protection data collection and monitoring control based on industrial internet of things of claim 9, wherein determining at least one stealth enterprise comprises:

11. Environmental protection data acquisition and monitoring control system based on industry thing networking, characterized by comprising:

12. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements an environmental protection data collection and monitoring control method based on industrial internet of things as claimed in any one of claims 1 to 10.

13. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the computer program realizes an environmental protection data collection and monitoring control method based on industrial internet of things according to any one of claims 1 to 10.