CN117557166B - Multi-mode and real-time data user data environment intelligent monitoring system - Google Patents
Multi-mode and real-time data user data environment intelligent monitoring system Download PDFInfo
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Abstract
The invention discloses a multi-mode and real-time data user data environment intelligent monitoring system, which particularly relates to the technical field of environment detection, and comprises a water surface image analysis module, a water surface ecological risk assessment module, a water quality appearance analysis module, a water quality appearance quality assessment module, an ecological risk analysis module, an ecological risk assessment module and a control module.
Description
Technical Field
The invention relates to the technical field of environment detection, in particular to an intelligent monitoring system for a multi-mode and real-time data user data environment.
Background
The current situation and the future development trend of the water quality can be accurately known through unified timing or non-timing detection of chemical substances, suspended matters, sediment and water ecology systems in the water, and when the water quality is abnormal, if the situation that the pollutant exceeds standard is generated, the water quality monitoring system can timely find and give out early warning so that relevant departments can take corresponding measures to prevent further expansion of pollution; the source and the diffusion path of the pollutants can be known by long-term monitoring of the water quality, and basis is provided for water pollution control, such as making a pollution control scheme, evaluating the treatment effect and the like.
However, in practical use, the existing environment detection system still has more defects, such as relying on manual data acquisition, analysis, sampling and evaluation; the collection frequency is low, so that the water quality monitoring is not timely enough, and the long-time monitoring on the water quality state is lacked, so that the water quality is abnormal; lack of evaluation of the control operation results in uncontrollable water quality control.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, the present invention provides a multi-modal and real-time data user data environment intelligent monitoring system, so as to solve the problems set forth in the above-mentioned background art.
Technical proposal
In order to achieve the above purpose, the present invention provides the following technical solutions: a multi-modal and real-time data user data environment intelligent monitoring system comprising:
And a data acquisition module: real-time data for monitoring water quality information, wherein the water quality information comprises water surface pictures, water quality color information and in-water suspended matter information, and in-water characteristic parameter information;
The water surface image analysis module: based on an image processing technology and a plant type recognition model, obtaining the type and the area of the water surface plant;
The water surface ecological risk assessment module: acquiring a water surface plant risk condition based on an analysis result of the water surface image analysis module;
The water quality appearance analysis module: acquiring a color difference value, a floating object content and a non-precipitable suspended object content of water based on the acquired water quality color information and the suspended object information in water;
The water quality appearance quality evaluation module: based on the analysis result obtained by the water quality appearance analysis module, evaluating the water quality appearance quality;
The ecological risk analysis module acquires a heavy metal exceeding coefficient and an ecological parameter deviation coefficient based on the acquired characteristic parameters;
the ecological risk assessment module is used for acquiring an ecological risk index and acquiring an ecological risk condition based on the acquired heavy metal exceeding coefficient and the ecological parameter deviation coefficient;
and the control module is used for: generating a control instruction based on the acquired water quality color difference value, the content of the floating matters, the heavy metal exceeding coefficient and the ecological parameter deviation coefficient, and controlling the water quality;
The water quality control quality evaluation module: the control quality evaluation module is used for evaluating the control quality of the control module, analyzing the control result of the control module to obtain a control quality evaluation coefficient, wherein the water quality control quality evaluation coefficient indicates the control condition of the water quality state, and when the water quality control quality evaluation coefficient is lower than a preset value, an early warning is sent to a manager to prompt abnormal control and abnormal water quality.
Preferably, the data acquisition mode is that a water area to be monitored is divided into n areas according to the area or the position, numbering is carried out, water quality information of each area is obtained, average value is obtained to obtain the water quality information, the data acquisition module comprises a water surface information acquisition unit, a water quality appearance information acquisition unit and a water quality characteristic parameter acquisition unit, the water surface information acquisition unit acquires water surface pictures through image acquisition equipment, and the acquired water surface pictures are transmitted to a water surface image analysis module; the water quality appearance information acquisition unit is used for acquiring the color information and suspended matter information of water and transmitting the acquired information to the water quality appearance analysis module; the water quality characteristic parameter acquisition unit is used for acquiring characteristic parameter information of water quality, wherein the characteristic parameters comprise oxygen content, pH, temperature and heavy metal content in water, and the acquired information is transmitted to the ecological risk monitoring module.
Preferably, in the data acquisition module, water surface image information, water quality color information, water quality suspended matter information and water quality characteristic parameters are automatically acquired through set monitoring points.
Preferably, the construction and training of the plant type recognition model comprises the following steps:
Step S01, data preparation: the aquatic plant image dataset after the plant type is marked is prepared according to 4:6, dividing the plant type into a training set and a testing set, wherein each plant type corresponds to a folder, and the folder contains images of the plant in different growth states;
step S02, data preprocessing: preprocessing the image data, wherein the preprocessing mode comprises image enhancement, normalization and enhancement image pixel level;
step S03, selecting a model frame: selecting a proper deep learning model framework for training according to actual problem scenes and data set characteristics, such as Convolutional Neural Network (CNN), cyclic neural network (RNN) and transfer learning;
Step S04, model training: inputting training set data into a plant category identification model for training, setting a loss function, and adjusting model learning rate, batch size and convolution layer number parameters according to the loss function, wherein the loss function is the difference value between the probability of the ith type of plant and the actual situation of the model output;
Step S05, model evaluation: and evaluating the trained plant type recognition model by using test set data, calculating the accuracy, precision and recall index of the model to determine the effectiveness and generalization capability of the model, and optimizing according to the evaluation result to obtain the trained plant type recognition model.
Preferably, the water surface plant risk index is used for representing the water surface plant risk condition, the water surface plant risk index is obtained by inputting a water surface image into a plant type identification model to obtain the plant type of the water surface, and the water surface plant risk index is obtained by a formulaObtaining a water surface plant risk index, wherein Zf represents the water surface plant risk index, zl i represents the risk coefficient of the ith water surface phytoplankton, lv i represents the growth speed of the ith water surface phytoplankton according to the setting of a manager, lm i represents the area of the ith water surface phytoplankton, and M represents the area of the monitored water surface.
Preferably, the water quality appearance analysis module comprises a water quality color analysis unit and a suspended matter analysis unit in water, wherein the water quality color analysis unit is used for obtaining the difference value between the color of water in the area to be monitored and the preset color; the in-water suspended matter analysis unit is used for acquiring the content of suspended matters and the content of non-precipitable suspended matters in water; the acquisition mode of the difference value between the color of the water in the monitoring area and the preset color is as follows: the RGB value of water in the area to be monitored is obtained and recorded as RGB Monitoring device = (r, g, b), the RGB Monitoring device value monitored in real time is compared with the standard RGB value, and the formula is passedObtaining a color difference value, wherein yc represents the color difference value, r represents the red value of water, g represents the green value of water, b represents the blue value of water, r 0 represents the red value preset value of water, g 0 represents the green value preset value of water, and b 0 represents the blue value preset value of water; the suspension content is obtained by the following steps: sampling water, directly filtering to obtain the mass of suspended matters in the water without standing and precipitating, calculating the content of suspended matters in the water according to a formula h= (m 1-m2）×1000×1000/V Sample ), wherein h represents the content of suspended matters in the water, m 1 represents the suspended matters+the filter membrane+the weight of the weighing bottle, m 2 represents the sum of the filter membrane and the weight of the weighing bottle, V Sample represents the volume of the water sample, sampling by a sampler, irradiating the water vertically by laser without standing and precipitating, calculating the content of suspended matters in the water according to reflected light, and obtaining the total weight of the suspended matters in the water according to a formula/>Calculating the content of suspended matters in water, wherein c represents the concentration of the suspended matters, K represents the molar absorption coefficient, which is related to the property of the absorbing matters and the wavelength lambda of incident light, and b represents the thickness of the absorbing layer and T represents the transmittance according to practical conditions; by the formula/>Calculating the content xh of suspended matters in water, wherein f 1 represents a suspended matter mass influence factor, f 2 represents a suspended matter concentration influence factor, and f 1≤1.0,0≤f2≤1.0,f1+f2 =1.0 is more than or equal to 0; the non-precipitable suspension content is obtained by the following steps: after the sampled water was allowed to stand for 3 to 6 hours, the obtained suspension content was noted as non-precipitable suspension content, and the non-precipitable suspension content in the water was noted as Bh.
Preferably, the water quality appearance evaluation index is indicative of the water quality appearance quality, and the water quality appearance evaluation index is obtained by: obtaining the color difference value of water, the content of suspended matters in water and the content of non-precipitable suspended matters in water, and obtaining the color difference value of water, the content of suspended matters in water and the content of non-precipitable suspended matters in water by a formulaObtaining a water quality appearance evaluation index, wherein Wy represents a water quality appearance abnormality evaluation parameter, yc represents a water quality color difference parameter, xh represents the content of suspended matters in water, bh represents the content of non-precipitable suspended matters in water, f 3 represents the influence coefficient of suspended matters, f 4 represents the influence coefficient of non-precipitable suspended matters, and f 3≤1.0,0≤f4 is more than 0 and less than or equal to 1.0, and a manager sets according to actual conditions.
Preferably, the method for obtaining the ecological parameter offset coefficient is as follows: acquiring real-time dissolved oxygen amount, temperature and pH in water, obtaining a water quality abnormality evaluation index based on the dissolved oxygen amount, the temperature and the pH in water, and obtaining a water quality abnormality evaluation index by a formulaCalculating an ecological parameter deviation coefficient Sz, wherein ry i represents dissolved oxygen in water, rc i represents carbon dioxide in water, ry 0 represents standard dissolved oxygen in water, pH 0 represents a preset pH value in water, and the standard dissolved oxygen in water is obtained according to Henry's law and temperature; the acquisition mode of the heavy metal exceeding coefficient is as follows: obtaining the type and the content of heavy metals in water, setting the upper limit of the content of each heavy metal, setting the risk coefficient of the heavy metal, and obtaining the heavy metal content in water by a formulaCalculating a heavy metal risk coefficient in water, wherein Zhs represents the heavy metal risk coefficient, h i represents the content of the ith type of heavy metal, h i0 represents the content limit value of the ith type of heavy metal, and fzi represents the risk coefficient of the ith type of heavy metal.
Preferably, the ecological risk condition is represented by an ecological risk index, and the ecological risk index is obtained by the following steps: acquiring an ecological parameter deviation coefficient and a heavy metal risk coefficient, and obtaining the ecological parameter deviation coefficient and the heavy metal risk coefficient through a formulaAn ecological risk index is obtained, wherein Sf represents the ecological risk index.
Preferably, the control module generates a control instruction based on the acquired water surface ecological risk assessment index, the water quality appearance quality assessment index and the ecological risk index, controls water quality disinfection, purification, precipitation and salvage operations through the instruction, sets starting conditions of the instruction, starts purification operation when the water quality color difference exceeds a preset value, and reduces the difference between the water quality color and the preset value by adding activated carbon adsorption color; when the risk index of the plants on the water surface exceeds a preset value, starting a salvaging operation to remove the plants on the water surface; when the content of suspended matters in water exceeds a preset value, starting a precipitation operation, and removing the suspended matters in water by adding an adsorbent; when the dissolved oxygen in water is lower than a preset value, starting the oxygenerator to increase the dissolved oxygen in water.
Preferably, the water quality control quality assessment module is formulated by the formulaObtaining a water quality control coefficient, wherein Ks represents the water quality control quality coefficient, w 1 represents a water surface state weight coefficient, w 2 represents a water quality appearance weight coefficient, w 3 represents a weight coefficient of a water surface characteristic parameter, and w 1+w2+w3=1.0,Zf Adjustment of represents an adjusted water surface plant risk index; wy Adjustment of represents the adjusted water surface plant risk index; sf Adjustment of represents an ecological risk index after adjustment, when the quality coefficient Ks of water quality control is lower than a preset value, the water quality control is abnormal, early warning is given to a manager, the manager is prompted to check the water quality change, and the execution condition of a control instruction is verified.
The invention has the technical effects and advantages that:
According to the multi-mode information on water quality is acquired through real-time acquisition, the control instruction is generated based on the acquired water quality color difference value, the floater content, the heavy metal exceeding coefficient and the ecological parameter deviation coefficient, the water quality is controlled, the water surface plant risk condition, the water quality appearance quality and the ecological risk condition are obtained through three-dimensional analysis of the water surface, the water appearance and the characteristic parameters of the water, the control quality of the control module is evaluated, the control result of the control module is analyzed, the control quality evaluation coefficient is obtained, the water quality control quality evaluation coefficient indicates the control condition of the water quality state, and when the water quality control quality evaluation coefficient is lower than a preset value, early warning is sent to management personnel to prompt abnormal control and abnormal water quality, so that the problems that water quality monitoring is not timely enough, long-time monitoring of the water quality state is lacked, evaluation of control operation is lacked, and water quality control is uncontrollable are solved.
Drawings
Fig. 1 is a block diagram showing the overall structure of the present invention.
FIG. 2 is a flow chart of the construction and training of the plant type recognition model of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
A computer system/server may be described in the general context of computer-system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc., that perform particular tasks or implement particular abstract data types. The computer system/server may be implemented in a distributed cloud computing environment in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computing system storage media including memory storage devices.
Examples: the invention provides an intelligent monitoring system for multi-mode and real-time data user data environment, as shown in figure 1, comprising: the system comprises a data acquisition module, a water surface image analysis module, a water surface ecological risk assessment module, a water quality appearance analysis module, a water quality appearance quality assessment module, an ecological risk analysis module, an ecological risk assessment module, a control module and a water quality control quality assessment module, wherein the data acquisition module comprises a water surface information acquisition unit, a water quality appearance information acquisition unit and a water quality characteristic parameter acquisition unit; the water surface image analysis module is used for analyzing and obtaining water surface plant information based on the data acquired by the water surface information acquisition unit; the water surface ecological risk assessment module obtains a water surface plant risk condition based on the analysis result of the water surface image analysis module and the monitored water surface basic information; the water quality appearance analysis module obtains the color difference value, the content of floaters and the content of non-precipitable suspended matters of the water quality based on the information acquired by the water quality appearance information acquisition unit, and transmits the analysis result to the water quality appearance quality assessment module, and the water quality appearance quality assessment module assesses the water quality appearance quality based on the analysis result; the ecological risk analysis module obtains characteristic parameter information based on the information acquired by the water quality characteristic parameter acquisition unit, and transmits an analysis result to the ecological risk assessment module; the ecological risk assessment module obtains an ecological risk condition based on the result of the ecological risk analysis module; the control module takes control measures based on analysis results of the water surface image analysis module, the water quality appearance analysis module and the ecological risk analysis module, and adjusts water quality parameters; the water quality control quality evaluation module is used for analyzing the water quality information before and after adjustment.
And a data acquisition module: real-time data for monitoring water quality information, wherein the water quality information comprises water surface pictures, water quality color information and in-water suspended matter information, and in-water characteristic parameter information;
The water surface image analysis module: based on an image processing technology and a plant type recognition model, obtaining the type and the area of the water surface plant;
The water surface ecological risk assessment module: acquiring a water surface plant risk condition based on an analysis result of the water surface image analysis module;
The water quality appearance analysis module: acquiring a color difference value, a floating object content and a non-precipitable suspended object content of water based on the acquired water quality color information and the suspended object information in water;
The water quality appearance quality evaluation module: based on the analysis result obtained by the water quality appearance analysis module, evaluating the water quality appearance quality;
The ecological risk analysis module acquires a heavy metal exceeding coefficient and an ecological parameter deviation coefficient based on the acquired characteristic parameters;
the ecological risk assessment module is used for acquiring an ecological risk index and acquiring an ecological risk condition based on the acquired heavy metal exceeding coefficient and the ecological parameter deviation coefficient;
and the control module is used for: generating a control instruction based on the acquired water quality color difference value, the content of the floating matters, the heavy metal exceeding coefficient and the ecological parameter deviation coefficient, and controlling the water quality;
The water quality control quality evaluation module: the control quality evaluation module is used for evaluating the control quality of the control module, analyzing the control result of the control module to obtain a control quality evaluation coefficient, wherein the water quality control quality evaluation coefficient indicates the control condition of the water quality state, and when the water quality control quality evaluation coefficient is lower than a preset value, an early warning is sent to a manager to prompt abnormal control and abnormal water quality.
The method for acquiring the data comprises the steps of dividing a water area to be monitored into n areas according to the area or the position, numbering the n areas, acquiring water quality information of each area, taking an average value to obtain the water quality information, acquiring a water surface picture by the water surface information acquisition unit through the image acquisition equipment, and transmitting the acquired water surface picture to the water surface image analysis module; the water quality appearance information acquisition unit is used for acquiring the color information and suspended matter information of water and transmitting the acquired information to the water quality appearance analysis module; the water quality characteristic parameter acquisition unit is used for acquiring characteristic parameter information of water quality, wherein the characteristic parameters comprise oxygen content, pH, temperature and heavy metal content in water, and the acquired information is transmitted to the ecological risk monitoring module.
It should be explained that, in the data acquisition module, the water surface image information, the water quality color information, the water quality suspended matter information and the water quality characteristic parameters are automatically acquired through the set monitoring points.
It should be noted that, as shown in fig. 2, the construction and training of the plant type recognition model includes the following steps:
Step S01, data preparation: the aquatic plant image dataset after the plant type is marked is prepared according to 4:6, dividing the plant type into a training set and a testing set, wherein each plant type corresponds to a folder, and the folder contains images of the plant in different growth states;
step S02, data preprocessing: preprocessing the image data, wherein the preprocessing mode comprises image enhancement, normalization and enhancement image pixel level;
step S03, selecting a model frame: according to the actual problem scene and the data set characteristics, setting a deep learning model frame for training;
Step S04, model training: inputting training set data into a plant category identification model for training, setting a loss function, and adjusting model learning rate, batch size and convolution layer number parameters according to the loss function, wherein the loss function is the difference value between the probability of the ith type of plant and the actual situation of the model output;
Step S05, model evaluation: and evaluating the trained plant type recognition model by using test set data, calculating the accuracy, precision and recall index of the model to determine the effectiveness and generalization capability of the model, and optimizing according to the evaluation result to obtain the trained plant type recognition model.
Further, the water surface plant risk index is used for representing the water surface plant risk condition, the water surface plant risk index is obtained by inputting a water surface image into a plant type identification model to obtain the plant type of the water surface, and the water surface plant risk index is obtained by a formulaObtaining a water surface plant risk index, wherein Zf represents the water surface plant risk index, zl i represents the risk coefficient of the ith water surface phytoplankton, lv i represents the growth speed of the ith water surface phytoplankton according to the setting of a manager, lm i represents the area of the ith water surface phytoplankton, and M represents the area of the monitored water surface.
Further, the water quality appearance analysis module comprises a water quality color analysis unit and a suspended matter analysis unit in water, wherein the water quality color analysis unit is used for obtaining the difference value between the color of water in the area to be monitored and the preset color; the in-water suspended matter analysis unit is used for acquiring the content of suspended matters and the content of non-precipitable suspended matters in water; the acquisition mode of the difference value between the color of the water in the monitoring area and the preset color is as follows: the RGB value of water in the area to be monitored is obtained and recorded as RGB Monitoring device = (r, g, b), the RGB Monitoring device value monitored in real time is compared with the standard RGB value, and the formula is passedObtaining a color difference value, wherein yc represents the color difference value, r represents the red value of water, g represents the green value of water, b represents the blue value of water, r 0 represents the red value preset value of water, g 0 represents the green value preset value of water, and b 0 represents the blue value preset value of water; the suspension content is obtained by the following steps: sampling water, directly filtering to obtain the mass of suspended matters in the water without standing and precipitating, calculating the content of suspended matters in the water according to a formula h= (m 1-m2）×1000×1000/V Sample ), wherein h represents the content of suspended matters in the water, m 1 represents the suspended matters+the filter membrane+the weight of the weighing bottle, m 2 represents the sum of the filter membrane and the weight of the weighing bottle, V Sample represents the volume of the water sample, sampling by a sampler, irradiating the water vertically by laser without standing and precipitating, calculating the content of suspended matters in the water according to reflected light, and obtaining the total weight of the suspended matters in the water according to a formula/>Calculating the content of suspended matters in water, wherein c represents the concentration of the suspended matters, K represents the molar absorption coefficient, which is related to the property of the absorbing matters and the wavelength lambda of incident light, and b represents the thickness of the absorbing layer and T represents the transmittance according to practical conditions; by the formula/>Calculating the content xh of suspended matters in water, wherein f 1 represents a suspended matter mass influence factor, f 2 represents a suspended matter concentration influence factor, and f 1≤1.0,0≤f2≤1.0,f1+f2 =1.0 is more than or equal to 0; the non-precipitable suspension content is obtained by the following steps: after the sampled water was allowed to stand for 3 to 6 hours, the obtained suspension content was noted as non-precipitable suspension content, and the non-precipitable suspension content in the water was noted as Bh.
Further, the water quality appearance evaluation index is used for indicating the water quality appearance quality, and the water quality appearance evaluation index is obtained by the following steps: obtaining the color difference value of water, the content of suspended matters in water and the content of non-precipitable suspended matters in water, and obtaining the color difference value of water, the content of suspended matters in water and the content of non-precipitable suspended matters in water by a formulaObtaining a water quality appearance evaluation index, wherein Wy represents a water quality appearance abnormality evaluation parameter, yc represents a water quality color difference parameter, xh represents the content of suspended matters in water, bh represents the content of non-precipitable suspended matters in water, f 3 represents the influence coefficient of suspended matters, f 4 represents the influence coefficient of non-precipitable suspended matters, and f 3≤1.0,0≤f4 is more than 0 and less than or equal to 1.0, and a manager sets according to actual conditions.
Further, the ecological parameter offset coefficient is obtained by the following steps: acquiring real-time dissolved oxygen amount, temperature and pH in water, obtaining a water quality abnormality evaluation index based on the dissolved oxygen amount, the temperature and the pH in water, and obtaining a water quality abnormality evaluation index by a formulaCalculating an ecological parameter deviation coefficient Sz, wherein ry i represents dissolved oxygen in water, rc i represents carbon dioxide in water, ry 0 represents standard dissolved oxygen in water, pH 0 represents a preset pH value in water, and the standard dissolved oxygen in water is obtained according to Henry's law and temperature; the acquisition mode of the heavy metal exceeding coefficient is as follows: obtaining the type and the content of heavy metals in water, setting the upper limit of the content of each heavy metal, setting the risk coefficient of the heavy metal, and obtaining the heavy metal content in water by a formulaCalculating a heavy metal risk coefficient in water, wherein Zhs represents the heavy metal risk coefficient, h i represents the content of the ith type of heavy metal, h i0 represents the content limit value of the ith type of heavy metal, and fzi represents the risk coefficient of the ith type of heavy metal.
Furthermore, the ecological risk index is used for representing the ecological risk condition, and the ecological risk index is obtained by the following steps: acquiring an ecological parameter deviation coefficient and a heavy metal risk coefficient, and obtaining the ecological parameter deviation coefficient and the heavy metal risk coefficient through a formulaAn ecological risk index is obtained, wherein Sf represents the ecological risk index.
Further, the control module generates a control instruction based on the acquired water surface ecological risk assessment index, the water quality appearance quality assessment index and the ecological risk index, controls water quality disinfection, purification, precipitation and salvage operations through the instruction, sets starting conditions of the instruction, for example, starts the purification operation when the water quality color difference exceeds a preset value, and reduces the difference between the water quality color and the preset value by adding activated carbon adsorption color; when the risk index of the plants on the water surface exceeds a preset value, starting a salvaging operation to remove the plants on the water surface; when the content of suspended matters in water exceeds a preset value, starting a precipitation operation, and removing the suspended matters in water by adding an adsorbent; when the dissolved oxygen in water is lower than a preset value, starting the oxygenerator to increase the dissolved oxygen in water.
Further, the water quality control quality evaluation module is based on the formulaObtaining a water quality control coefficient, wherein Ks represents the water quality control quality coefficient, w 1 represents a water surface state weight coefficient, w 2 represents a water quality appearance weight coefficient, w 3 represents a weight coefficient of a water surface characteristic parameter, and w 1+w2+w3=1.0,Zf Adjustment of represents an adjusted water surface plant risk index; wy Adjustment of represents the adjusted water surface plant risk index; sf Adjustment of represents the adjusted ecological risk index.
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 (6)
1. An intelligent monitoring system for a multi-modal and real-time data user data environment, comprising:
and a data acquisition module: real-time data for monitoring water quality information, wherein the water quality information comprises water surface pictures, water quality color information, in-water suspended matter information and in-water characteristic parameter information; the characteristic parameter information in the water comprises oxygen content, pH, temperature and heavy metal content in the water, and the acquired information is transmitted to an ecological risk analysis module;
The water surface image analysis module: based on an image processing technology and a plant type recognition model, obtaining the type and the area of the water surface plant;
The water surface ecological risk assessment module: acquiring a water surface plant risk condition based on an analysis result of the water surface image analysis module;
the water quality appearance analysis module: acquiring water quality color difference parameters, suspended matter content in water and non-precipitable suspended matter content in water based on the acquired water quality color information and the suspended matter information in water; the water quality appearance analysis module comprises a water quality color analysis unit and an in-water suspended matter analysis unit, wherein the water quality color analysis unit is used for acquiring water quality color difference parameters in a region to be monitored; the in-water suspended matter analysis unit is used for acquiring the content of suspended matters in water and the content of non-precipitable suspended matters in water; the acquisition mode of the water quality color difference parameters in the monitoring area is as follows: the RGB value of water in the area to be monitored is obtained and recorded as RGB Monitoring device = (r, g, b), the RGB Monitoring device value monitored in real time is compared with the standard RGB value, and the formula is passed Obtaining a water quality color difference parameter, wherein yc represents the water quality color difference parameter, r represents the red value of water, g represents the green value of water, b represents the blue value of water, r 0 represents the red value preset value of water, g 0 represents the green value preset value of water, and b 0 represents the blue value preset value of water; the acquisition mode of the suspended matter content in water is as follows: taking a water sample, directly filtering to obtain the mass of suspended matters in water without standing and precipitating, calculating the mass of suspended matters in water according to a formula h= (m 1-m2）×1000×1000/V Sample , wherein h represents the mass of suspended matters in water, m 1 = the mass of suspended matters + the mass of a filter membrane + the mass of a weighing bottle, m 2 = the mass of the filter membrane + the mass of the weighing bottle, and V Sample represents the volume of the water sample, sampling by a sampler, irradiating the water vertically by laser without standing and precipitating, calculating the content of suspended matters in the water according to reflected light, and obtaining the total weight of the suspended matters in water according to the formula/>Calculating the concentration of suspended matters in water, wherein c represents the concentration of suspended matters in water, K represents the molar absorption coefficient, which is related to the property of the absorption matters and the wavelength lambda of incident light, and is set according to practical conditions, b represents the thickness of an absorption layer, and T represents the transmittance; by the formula/>Calculating the suspended matter content xh in water, wherein f 1 represents a suspended matter quantity influence factor, f 2 represents a suspended matter concentration influence factor, and f 1≤1.0,0≤f2≤1.0,f1+f2 =1.0 is more than or equal to 0; the acquisition mode of the content of the non-precipitable suspended substances in the water is as follows: standing the sampled water for 3-6h, and marking the obtained content of the suspended matters as the content of non-precipitable suspended matters in the water, and marking the content of the non-precipitable suspended matters in the water as Bh;
The water quality appearance quality evaluation module: based on the analysis result obtained by the water quality appearance analysis module, evaluating the water quality appearance quality; the water quality appearance abnormal evaluation index is used for representing the water quality appearance quality, and the water quality appearance abnormal evaluation index is obtained by the following steps: obtaining the water quality color difference parameters, the suspended matter content in water and the non-precipitable suspended matter content in water, and passing through the formula Obtaining a water quality appearance abnormality evaluation index, wherein Wy represents the water quality appearance abnormality evaluation index, yc represents a water quality color difference parameter, xh represents the content of suspended matters in water, bh represents the content of non-precipitable suspended matters in water, f 3 represents the influence coefficient of suspended matters, f 4 represents the influence coefficient of non-precipitable suspended matters, and f 3≤1.0,0≤f4 is more than 0 and less than or equal to 1.0, and a manager sets according to actual conditions;
the ecological risk analysis module acquires a heavy metal exceeding coefficient and an ecological parameter deviation coefficient based on the acquired characteristic parameter information in the water;
the ecological parameter offset coefficient is obtained by the following steps: acquiring real-time dissolved oxygen amount, temperature and pH in water, obtaining a water quality abnormality evaluation index based on the dissolved oxygen amount, the temperature and the pH in water, and obtaining a water quality abnormality evaluation index by a formula Calculating an ecological parameter deviation coefficient Sz, wherein ry i represents dissolved oxygen in water, rc i represents carbon dioxide in water, ry 0 represents standard dissolved oxygen in water, pH 0 represents a preset pH value in water, and the standard dissolved oxygen in water is obtained according to Henry's law and temperature; the acquisition mode of the heavy metal exceeding coefficient is as follows: obtaining the type and the content of heavy metals in water, setting the upper limit of the content of each heavy metal, setting the risk coefficient of the heavy metal, and obtaining the heavy metal content by the formula/>Calculating a heavy metal exceeding coefficient, wherein Zhs represents the heavy metal exceeding coefficient, h j represents the content of the j-th type heavy metal, h j0 represents the content limit value of the j-th type heavy metal, and fzj represents the risk coefficient of the j-th type heavy metal;
The ecological risk assessment module is used for acquiring an ecological risk index and acquiring an ecological risk condition based on the acquired heavy metal exceeding coefficient and the ecological parameter deviation coefficient; the ecological risk index is used for representing the ecological risk condition, and the ecological risk index is obtained by the following steps: acquiring an ecological parameter deviation coefficient and a heavy metal exceeding coefficient, and obtaining the ecological parameter deviation coefficient and the heavy metal exceeding coefficient through a formula Obtaining an ecological risk index, wherein Sf represents the ecological risk index;
and the control module is used for: generating a control instruction based on the acquired water quality color difference parameters, the content of suspended matters in water, the heavy metal exceeding coefficient and the ecological parameter deviation coefficient, and controlling the water quality;
The water quality control quality evaluation module: the control module is used for evaluating the control quality of the control module, analyzing the control result of the control module to obtain a water quality control quality evaluation coefficient, wherein the water quality control quality evaluation coefficient indicates the control condition of the water quality state, and when the water quality control quality evaluation coefficient is lower than a preset value, the control module sends an early warning to a manager to prompt abnormal control and abnormal water quality.
2. The intelligent monitoring system for the multi-mode and real-time data user data environment according to claim 1 is characterized in that the data acquisition mode is that a water area to be monitored is divided into n areas according to areas or positions, the n areas are numbered, water quality information of each area is obtained, the average value of the water quality information is obtained, the data acquisition module comprises a water surface information acquisition unit, a water quality appearance information acquisition unit and a water quality characteristic parameter acquisition unit, the water surface information acquisition unit acquires water surface pictures through an image acquisition device, and the acquired water surface pictures are transmitted to a water surface image analysis module; the water quality appearance information acquisition unit is used for acquiring the color information and the suspended matter information of the water and transmitting the acquired information to the water quality appearance analysis module; the water quality characteristic parameter acquisition unit is used for acquiring characteristic parameter information of water quality.
3. The intelligent monitoring system for multi-modal and real-time data user data environments of claim 1, wherein the building and training of the plant type recognition model comprises the steps of:
Step S01, data preparation: the aquatic plant image dataset after the plant type is marked is prepared according to 4:6, dividing the plant type into a training set and a testing set, wherein each plant type corresponds to a folder, and the folder contains images of the plant in different growth states;
Step S02, data preprocessing: preprocessing the image data, wherein the preprocessing mode comprises image enhancement, normalization and enhancement image pixel level;
step S03, selecting a model frame: according to the actual problem scene and the data set characteristics, setting a deep learning model frame for training;
step S04, model training: inputting training set data into a plant category identification model for training, setting a loss function, and adjusting model learning rate, batch size and convolution layer number parameters according to the loss function, wherein the loss function is a difference value between the probability of the ith water surface phytoplankton and the actual situation;
step S05, model evaluation: and evaluating the trained plant type recognition model by using test set data, and calculating the accuracy, precision and recall index of the model to determine the effectiveness and generalization capability of the model, and optimizing according to the evaluation result to obtain the trained plant type recognition model.
4. The intelligent monitoring system for user data environment with multiple modes and real time data according to claim 1, wherein the risk of water surface plant is represented by a risk index of water surface plant, which is obtained by inputting a water surface image into a plant type recognition model to obtain a plant type of water surface, and the method comprises the following steps ofObtaining a water surface plant risk index, wherein Zf represents the water surface plant risk index, zl i represents the risk coefficient of the ith water surface phytoplankton, lv i represents the growth speed of the ith water surface phytoplankton according to the setting of a manager, lm i represents the area of the ith water surface phytoplankton, and M represents the area of the monitored water surface.
5. The intelligent monitoring system for multi-modal and real-time data consumer data environments of claim 4, wherein the control module generates control instructions based on the acquired water surface plant risk index, water quality appearance anomaly evaluation index, and ecological risk index, controls water quality, and controls water quality disinfection, purification, sedimentation, and salvage operations via the instructions.
6. The intelligent monitoring system for multi-modal and real-time data user data environment of claim 1, wherein the quality of water quality control assessment module is formulated by the formulaObtaining a water quality control quality evaluation coefficient, wherein Ks represents the water quality control quality evaluation coefficient, w 1 represents a water surface state weight coefficient, w 2 represents a water quality appearance weight coefficient, w 3 represents a weight coefficient of a water surface characteristic parameter, and w 1+w2+w3=1.0,Zf Adjustment of represents an adjusted water surface plant risk index; wy Adjustment of represents the adjusted water quality appearance abnormality evaluation index; sf Adjustment of represents the adjusted ecological risk index.
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