CN115436374B - Leachate sewage monitoring system based on Internet of things - Google Patents
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Abstract
The invention discloses a leachate sewage monitoring system based on the Internet of things, which relates to the technical field of leachate harmful substance monitoring, and is characterized in that an image analysis module is arranged to obtain an image of leachate generated by garbage accumulation, and a CNN neural network model is used for analyzing the probability of the leachate being sewage; under the condition that the garbage is judged to be sewage, collecting air at the garbage accumulation position through a gas analysis module, and analyzing whether the air contains harmful gas or not; if the liquid contains harmful gas, collecting percolate liquid at the confluence position of surrounding liquid through a percolate analysis module, and analyzing whether the liquid contains harmful substances or not; if the harmful substances are contained, sending an early warning signal to a control terminal; sending an alarm signal to a city manager by a control terminal; the automatic monitoring problem of the harmful substances in the percolate is solved.
Description
Technical Field
The invention belongs to the field of leachate detection, relates to the technology of the Internet of things, and particularly relates to a leachate sewage monitoring system based on the Internet of things.
Background
During the stacking and burying process of the garbage, due to the biochemical degradation effects of compaction, fermentation and the like, a high-concentration liquid with organic or inorganic components is generated under the seepage action of precipitation and underground water, and the high-concentration liquid is called as garbage leachate, also called as leachate. Factors influencing the generation of percolate are many, and mainly include rainfall condition of a garbage stacking and burying area, the property and the composition of garbage, seepage-proofing treatment condition of a landfill site, hydrogeological conditions of the site and the like; when toxic substances or high-volatility pungent substances exist in the garbage deposits, the lives of surrounding residents are often influenced; the toxic leachate can be discovered only when the residential life is affected after the leachate has been percolated for a period of time; therefore, there is a lack of a monitoring system for detecting the inclusion of harmful substances in the leachate in advance;
therefore, a leachate sewage monitoring system based on the Internet of things is provided.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. The leachate sewage monitoring system based on the Internet of things is provided with an image analysis module to obtain an image of leachate generated by garbage accumulation, and a CNN neural network model is used for analyzing the probability of the leachate being sewage; under the condition that the garbage is judged to be sewage, collecting air at the garbage accumulation position through a gas analysis module, and analyzing whether the air contains harmful gas or not; if the liquid contains harmful gas, collecting percolate liquid at the confluence position of surrounding liquid through a percolate analysis module, and analyzing whether the liquid contains harmful substances or not; if the harmful substances are contained, sending an early warning signal to a control terminal; sending an alarm signal to a city manager by a control terminal; the automatic monitoring problem of the harmful substances in the percolate is solved.
In order to achieve the above object, an embodiment according to a first aspect of the present invention provides a leachate sewage monitoring system based on the internet of things, including an image analysis module, a gas analysis module, a leachate analysis module, and a control terminal;
the image analysis module is mainly used for analyzing the probability of harmfulness of the percolate according to the image of the percolate generated by the garbage accumulation;
the image analysis module comprises an image acquisition unit and an image analysis unit;
the image acquisition unit is mainly used for collecting video pictures of the outflow position of the percolate;
the image acquisition unit comprises a plurality of image acquisition devices arranged around the garbage stacking position; the image acquisition unit can be a monitoring camera; each image acquisition device acquires ground video data in a visual field in real time; it can be understood that when the ground with garbage is piled up, the penetrating fluid can be shot by the image acquisition equipment; each image acquisition unit electrically transmits acquired video data to a corresponding image analysis unit;
wherein, the image analysis unit is mainly used for calculating the harmful probability of the percolate according to the percolate image;
the image analysis unit for calculating the leachate hazard probability comprises the following steps:
step S1: the control terminal collects a plurality of water flow pictures in advance; marking sewage and non-sewage in the sewage by a manual marking mode; marking sewage as 1 and non-sewage as 0;
step S2: the control terminal inputs the water flow picture into the CNN neural network model as input; the CNN neural network model takes the predicted probability that water flow is sewage as output, and the predicted accuracy as a training target; training a CNN neural network model; parameter setting and parameter adjustment of the CNN neural network model are configured according to actual experience;
and step S3: the control terminal stops training when the accuracy of the CNN neural network model is trained to 98%; marking the trained CNN neural network model as M; the control terminal sends the CNN neural network model M to each image analysis unit;
and step S4: each image analysis unit receives video data sent by the image acquisition unit; extracting a frame of picture from the video data at intervals of a time period T; inputting the extracted picture into a CNN neural network model M as input to obtain the probability p that the in-picture percolate is sewage; wherein the time period T is set according to actual experience;
step S5: the image analysis unit presets a probability threshold value P; judging whether the probability P is greater than a probability threshold value P, and if so, sending a gas detection signal to a gas analysis module;
the gas analysis module is mainly used for collecting gas in the air and detecting gas components;
the gas analysis module comprises a first control unit, a gas collection unit and a gas analysis unit; the first control unit is used for receiving and sending control signals;
the gas collection unit comprises a plurality of air collection devices arranged at the garbage accumulation position; the first control unit starts all the air collecting devices to collect the air at the garbage accumulation position after receiving the gas detection signal sent by the image analysis unit; the gas collecting unit is preset with a gas volume threshold value V1; when the volume of the air collected by the air collecting equipment reaches a gas volume threshold value V1, stopping collecting; each gas collection unit transports the collected air to a corresponding gas analysis unit through a pipeline;
the gas analysis unit is mainly used for analyzing whether harmful gas is contained in the air or not;
the gas analysis unit comprises a set of gas inspection equipment for inspecting harmful gases; the detected harmful gas category is set according to the category of substances with volatility commonly found in the percolate; the gas analysis unit detects the air by using gas detection equipment after receiving the air transmitted by the gas collection unit; if any harmful gas is detected to be contained in the air, sending a leachate analysis signal to a leachate analysis module;
the leachate analysis module is mainly used for analyzing whether leachate is harmful sewage or not;
the leachate analysis module comprises a second control unit, a leachate acquisition unit and a leachate analysis unit; the percolate collecting unit and the percolate analyzing unit are in one-to-one correspondence;
the leachate collecting unit is mainly used for collecting leachate generated at a garbage accumulation position;
the percolate collecting unit comprises a liquid collecting device which is arranged at a liquid converging position near the garbage accumulation position according to actual experience; the second control unit controls the liquid collection equipment to collect percolate liquid at the confluence part after receiving the percolate analysis signal; the leachate acquisition unit presets a liquid volume threshold value V2; stopping collecting when the liquid volume collected by the liquid collecting equipment reaches a liquid volume threshold value V2; each liquid collecting unit conveys the collected liquid to a corresponding percolate analyzing unit through a pipeline;
wherein, the percolate analyzing unit is mainly used for analyzing whether harmful gas is contained in the air;
the percolate analysis unit comprises a set of chemical detection equipment for detecting harmful substances; the detected harmful substance category is set according to the common harmful substance category in the percolate; the leachate analysis unit detects the liquid by using chemical inspection equipment after receiving the liquid transmitted by the leachate acquisition unit; if any harmful substance is detected to be contained in the liquid, sending an early warning signal to a control terminal; the early warning signal comprises the detected position of the percolate and the name of a toxic substance;
the control terminal is mainly used for initiating early warning to city management personnel when harmful substances are detected in the percolate;
after receiving the early warning signal, the control terminal sends an alarm signal to a city manager in a wireless and/or wired network mode; the alarm signal comprises the position of the harmful garbage accumulation of the percolate and the name of a toxic substance.
Compared with the prior art, the invention has the beneficial effects that:
1. the method comprises the steps of setting an image analysis module to obtain an image of leachate generated by garbage accumulation, and analyzing the probability of the leachate being sewage by using a CNN neural network model; under the condition that the garbage is judged to be sewage, collecting air at the garbage accumulation position through a gas analysis module, and analyzing whether the air contains harmful gas or not; if the liquid contains harmful gas, collecting percolate liquid at the confluence position of surrounding liquid through a percolate analysis module, and analyzing whether the liquid contains harmful substances or not; if the harmful substances are contained, sending an early warning signal to a control terminal; sending an alarm signal to a city manager by a control terminal; the problem of automatic monitoring of harmful substances in the leachate is solved;
2. according to the invention, through a layer-by-layer screening mode of firstly carrying out image analysis, then carrying out gas analysis and finally analyzing liquid, the detection accuracy of the percolate is improved, the detection times of gas and liquid are reduced, and the gas detection and liquid detection costs are reduced.
Drawings
Fig. 1 is a schematic diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a leachate sewage monitoring system based on the internet of things comprises an image analysis module, a gas analysis module, a leachate analysis module and a control terminal;
it can be understood that leachate is due to biochemical degradation of garbage during stacking and landfill, such as compaction, fermentation and the like, and a high-concentration liquid with organic or inorganic components is generated under the seepage action of precipitation and groundwater; therefore, the liquid percolation condition at the garbage stacking position needs to be monitored;
the image analysis module is mainly used for analyzing the probability of harmfulness of the percolate according to the image of the percolate generated by the garbage accumulation;
the image analysis module comprises an image acquisition unit and an image analysis unit; the image acquisition unit and the image analysis unit are combined and configured at positions where a plurality of percolate can appear according to the actual situation of a garbage accumulation position;
the image acquisition unit is mainly used for collecting video pictures of the outflow position of the percolate;
in a preferred embodiment, the image acquisition unit comprises a plurality of image acquisition devices arranged around the refuse dump; the image acquisition unit can be a monitoring camera; each image acquisition device acquires ground video data in a visual field in real time; it can be understood that when the ground with garbage is piled up, the penetrating fluid can be shot by the image acquisition equipment; each image acquisition unit electrically transmits acquired video data to the corresponding image analysis unit;
wherein, the image analysis unit is mainly used for calculating the harmful probability of the percolate according to the percolate image;
it is understood that generally toxic leachate includes some toxic colored organic or inorganic substances, which may cause the leachate to have special visual effects such as color and foam; whether the leachate is harmful or not can be analyzed according to the image of the leachate; but the visual standard of harmful percolate is reduced by considering that certain missing report rate exists in image identification; firstly, judging whether the sewage is the sewage or not through a percolate image, and further judging whether the sewage is the harmful sewage or not;
in a preferred embodiment, the image analysis unit calculating the leachate hazard probability comprises the following steps:
step S1: the control terminal collects a plurality of water flow pictures in advance; marking sewage and non-sewage in the sewage by a manual marking mode; marking the sewage as 1 and the non-sewage as 0;
step S2: the control terminal inputs the water flow picture into the CNN neural network model as input; the CNN neural network model takes the predicted probability that water flow is sewage as output, and the predicted accuracy as a training target; training a CNN neural network model; parameter setting and parameter adjustment of the CNN neural network model are configured according to actual experience;
and step S3: the control terminal stops training when the accuracy of the CNN neural network model is trained to 98%; marking the trained CNN neural network model as M; the control terminal sends the CNN neural network model M to each image analysis unit;
and step S4: each image analysis unit receives video data sent by the image acquisition unit; extracting a frame of picture from the video data at intervals of a time period T; inputting the extracted picture into a CNN neural network model M as input to obtain the probability p that the in-picture percolate is sewage; wherein the time period T is set according to actual experience;
step S5: the image analysis unit presets a probability threshold value P; judging whether the probability P is greater than a probability threshold value P, and if so, sending a gas detection signal to a gas analysis module;
the gas analysis module is mainly used for collecting gas in the air and detecting gas components;
in a preferred embodiment, the gas analysis module comprises a first control unit, a gas collection unit and a gas analysis unit; the first control unit is used for receiving and sending control signals; the gas collecting unit and the gas analyzing unit are combined one by one;
the gas collection unit comprises a plurality of air collection devices arranged at the garbage accumulation position; the first control unit starts all the air collecting devices to collect air at the garbage accumulation position after receiving the gas detection signal sent by the image analysis unit; the gas collecting unit is preset with a gas volume threshold value V1; stopping collecting when the volume of the air collected by the air collecting equipment reaches a gas volume threshold value V1; each gas collection unit transports the collected air to a corresponding gas analysis unit through a pipeline;
the gas analysis unit is mainly used for analyzing whether harmful gas is contained in the air or not;
in a preferred embodiment, the gas analysis unit comprises a set of gas inspection equipment for inspecting harmful gases; the detected harmful gas category is set according to the volatile substance category commonly seen in the percolate; the gas analysis unit detects the air by using gas detection equipment after receiving the air transmitted by the gas collection unit; if any harmful gas is detected to be contained in the air, sending a leachate analysis signal to a leachate analysis module;
the leachate analysis module is mainly used for analyzing whether leachate is harmful sewage or not;
the leachate analysis module comprises a second control unit, a leachate acquisition unit and a leachate analysis unit; the percolate collecting unit and the percolate analyzing unit are in one-to-one correspondence;
the leachate collection unit is mainly used for collecting leachate liquid generated at a garbage accumulation position;
in a preferred embodiment, the percolate collection unit comprises a liquid collection device installed on practical experience in the vicinity of the refuse deposit at a liquid confluence location; the second control unit controls the liquid collection equipment to collect percolate liquid at the confluence part after receiving the percolate analysis signal; the leachate acquisition unit is preset with a liquid volume threshold value V2; stopping collecting when the liquid volume collected by the liquid collecting equipment reaches a liquid volume threshold value V2; each liquid collecting unit conveys the collected liquid to a corresponding percolate analyzing unit through a pipeline;
wherein, the percolate analyzing unit is mainly used for analyzing whether harmful gas is contained in the air;
in a preferred embodiment, the leachate analysis unit comprises a set of chemical inspection devices for inspecting the harmful substances; the detected harmful substance types are set according to the common harmful substance types in the percolate; the leachate analysis unit detects the liquid by using chemical inspection equipment after receiving the liquid transmitted by the leachate acquisition unit; if any harmful substance is detected to be contained in the liquid, sending an early warning signal to a control terminal; the early warning signal comprises the detected position of the percolate and the name of a toxic substance;
the control terminal is mainly used for initiating early warning to city management personnel when harmful substances are detected in the percolate;
in a preferred embodiment, after receiving the early warning signal, the control terminal sends an alarm signal to a city manager in a wireless and/or wired network mode; the alarm signal comprises the position of the harmful garbage accumulation of the percolate and the name of a toxic substance.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (8)
1. A leachate sewage monitoring system based on the Internet of things is characterized by comprising an image analysis module, a gas analysis module, a leachate analysis module and a control terminal;
the image analysis module acquires an image of leachate generated by garbage accumulation, and analyzes the probability of the leachate being sewage by using a CNN neural network model; the image analysis module comprises an image acquisition unit and an image analysis unit; the image analysis module sends a gas detection signal to the gas analysis module when judging that the leachate is sewage;
the gas analysis module is used for collecting gas in the air and detecting gas components; the gas analysis module comprises a first control unit, a gas collection unit and a gas analysis unit; when the gas analysis module detects that harmful gas is contained in the air, sending a leachate analysis signal to the leachate analysis module;
the leachate analysis module is used for collecting leachate and analyzing whether the leachate is harmful sewage or not; the leachate analysis module comprises a second control unit, a leachate acquisition unit and a leachate analysis unit; the percolate collecting unit and the percolate analyzing unit are in one-to-one correspondence; when the leachate analysis module detects that the liquid contains harmful substances, an early warning signal is sent to a control terminal;
the control terminal is used for training a CNN neural network model for judging whether the percolate is sewage or not and initiating early warning to city management personnel when detecting that the percolate contains harmful substances.
2. The Internet of things-based leachate sewage monitoring system of claim 1, wherein the image acquisition unit comprises a plurality of image acquisition devices installed around the landfill site; each image acquisition device acquires ground video data in a visual field in real time; and each image acquisition unit electrically transmits the acquired video data to the corresponding image analysis unit.
3. The Internet of things-based leachate sewage monitoring system of claim 1, wherein the image analysis unit calculating the leachate hazard probability comprises the steps of:
step S1: the control terminal collects a plurality of water flow pictures in advance; marking sewage and non-sewage in the sewage by a manual marking mode; marking the sewage as 1 and the non-sewage as 0;
step S2: the control terminal inputs the water flow picture into the CNN neural network model as input; the CNN neural network model takes the predicted probability that water flow is sewage as output, and the predicted accuracy as a training target; training a CNN neural network model; parameter setting and parameter adjustment of the CNN neural network model are configured according to actual experience;
and step S3: the control terminal stops training when the accuracy of the CNN neural network model is trained to 98%; marking the trained CNN neural network model as M; the control terminal sends the CNN neural network model M to each image analysis unit;
and step S4: each image analysis unit receives video data sent by the image acquisition unit; extracting a frame of picture from the video data every time period T; inputting the extracted picture as input into a CNN neural network model M to obtain the probability p that the percolate in the picture is sewage; wherein the time period T is set according to actual experience;
step S5: the image analysis unit presets a probability threshold value P; and judging whether the probability P is greater than a probability threshold value P, and if so, sending a gas detection signal to a gas analysis module.
4. The leachate sewage monitoring system according to claim 1, wherein said gas collection unit comprises a plurality of air collection devices installed at a landfill site; the first control unit starts all the air collecting devices to collect the air at the garbage accumulation position after receiving the gas detection signal sent by the image analysis unit; the gas collecting unit is preset with a gas volume threshold value V1; stopping collecting when the volume of the air collected by the air collecting equipment reaches a gas volume threshold value V1; each gas collection unit transports the collected air to a corresponding gas analysis unit through a pipeline.
5. The Internet of things-based leachate sewage monitoring system according to claim 1, wherein the gas analysis unit comprises a set of gas inspection equipment for inspecting harmful gases; the detected harmful gas category is set according to the category of substances with volatility commonly found in the percolate; the gas analysis unit detects the air by using gas detection equipment after receiving the air transmitted by the gas collection unit; if any harmful gas is detected to be contained in the air, a leachate analysis signal is sent to the leachate analysis module.
6. The internet of things-based leachate sewage monitoring system according to claim 1, wherein the leachate collection unit comprises a liquid collection device installed at a liquid confluence location near a landfill site according to practical experience; the second control unit controls the liquid collection equipment to collect percolate liquid at the confluence part after receiving the percolate analysis signal; the leachate acquisition unit is preset with a liquid volume threshold value V2; stopping collecting when the liquid volume collected by the liquid collecting equipment reaches a liquid volume threshold value V2; each percolate collection unit conveys collected liquid to a corresponding percolate analysis unit through a pipeline.
7. The internet of things-based leachate sewage monitoring system according to claim 1, wherein said leachate analysis unit comprises a set of chemical inspection equipment for inspecting hazardous substances; the detected harmful substance category is set according to the common harmful substance category in the percolate; the leachate analysis unit detects the liquid by using chemical inspection equipment after receiving the liquid transmitted by the leachate acquisition unit; if the liquid is detected to contain any harmful substance, sending an early warning signal to a control terminal; the early warning signal comprises the detected position of the percolate and the name of a toxic substance.
8. The leachate sewage monitoring system based on the internet of things of claim 1, wherein the control terminal sends an alarm signal to a city manager in a wireless and/or wired network mode after receiving the early warning signal; the alarm signal comprises the position of the harmful garbage accumulation of the percolate and the name of a toxic substance.
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Denomination of invention: A leachate wastewater monitoring system based on the Internet of Things Effective date of registration: 20230810 Granted publication date: 20230324 Pledgee: Bank of Changsha Co.,Ltd. Jincheng sub branch Pledgor: Hunan Diya Environmental Engineering Co.,Ltd. Registration number: Y2023980051744 |