Suspicious clue automatic monitoring data analysis method based on artificial intelligence

Abstract

The invention relates to a suspicious clue automatic monitoring data analysis method based on artificial intelligence, and belongs to the field of electrical digital data processing, and the method comprises the steps: executing a set number of each learning action on a convolutional neural network to obtain a suspicious clue automatic monitoring model; intelligently analyzing whether a target monitoring water surface belongs to a polluted water surface or not and a suspected distance from a pollution source to a current monitoring point by adopting a suspicious clue automatic monitoring model based on time-sharing dynamic water quality related data and time-sharing dynamic visual data; according to the invention, the technical problems that environmental pollution monitoring data analysis results are not rich enough and the analysis process is complex are solved; an automatic suspicious clue monitoring model designed in a targeted structure can be used for carrying out intelligent data analysis on suspicious clues such as whether environment pollution exists in a fixed water area and the distance from an environment pollution source to the fixed water area based on time-sharing dynamic water quality data and time-sharing dynamic visual data, so that the technical problems are solved.

Description

Technical Field

[0001] The present invention relates to the field of electronic digital data processing, and in particular to an artificial intelligence-based suspicious clue automatic monitoring data analysis method. Background Art

[0002] With the acceleration of industrialization and the enhancement of environmental protection awareness, automatic monitoring technology for pollution sources has become an indispensable part of environmental management. Traditional pollution monitoring methods mainly rely on manual sampling and laboratory analysis, which is time-consuming, costly and inefficient. In recent years, with the development of modern information technologies such as the Internet of Things (IoT), big data and artificial intelligence (AI), automatic monitoring systems have begun to be widely used, which can realize real-time monitoring of pollutant emissions, overcoming the various defects of traditional pollution monitoring methods, such as high data processing complexity, high probability of false alarms and missed alarms, insufficient data analysis depth, slow response speed, easy waste of human and material resources, low data utilization, poor user experience, unreliable data quality, poor scalability and flexibility.

[0003] For example, the invention patent with publication number CN112101789A proposes a method for identifying water pollution alarm levels based on artificial intelligence, which includes the following steps: Step A: Acquire historical data and pre-process the historical data; Step B: Manually mark the risk level of historical data to obtain risk label data, and divide it into training set and test set in proportion; Step C: Build a deep learning model, input the training set data into the deep learning model, and output the trained model; Step D: Use the test set for testing, and if it meets the preset standards, it is used as a water pollution alarm level identification model; Step E: Identify the real-time data of each entry node to obtain the water pollution alarm level. The advantages of the present invention are: comprehensively consider the water quality and water quantity data of the water body, based on artificial intelligence deep learning technology, find the deep relationship between water pollution and multiple parameters, and accurately judge and identify the pollution alarm level.

[0004] For example, the invention patent with publication number CN109631998A proposes an environment artificial intelligence monitoring system and environment artificial intelligence monitoring method, the system includes: a particle concentration parameter acquisition module for collecting particle concentration parameters; a meteorological five parameter acquisition module for collecting meteorological five parameters; a noise parameter acquisition module for collecting noise parameters; an image parameter acquisition module for collecting image parameters; a particle pollution judgment module for editing the particle pollution judgment result and sending it to the alarm information editing module; a meteorological pollution judgment module for editing the meteorological pollution judgment result and sending it to the alarm information editing module; a noise pollution judgment module for editing the noise pollution judgment result and sending it to the alarm information editing module; an alarm information editing module for editing the corresponding alarm information and sending it to the terminal; an image display module for displaying the received image parameters. The present invention has the advantages of simple structure and easy implementation.

[0005] It can be seen that the various automatic environmental pollution monitoring data analysis schemes in the existing technology, although they adopt artificial intelligence mechanisms, analyze real-time water quality data, and are unable to judge environmental pollution and preliminarily locate the source of environmental pollution based on time-sharing water quality data and richer visual data; or although water quality data and visual data are used to jointly judge environmental pollution, artificial intelligence mechanisms are not adopted, and the preliminary location of environmental pollution sources cannot be achieved. Therefore, the analysis results either lack pertinence or still have various defects of traditional pollution monitoring methods. Summary of the invention

[0006] In order to solve the technical problems in the prior art, the present invention provides a suspicious clue automatic monitoring data analysis method based on artificial intelligence. For fixed waters suspected of water pollution, a suspicious clue automatic monitoring model with a targeted structural design is used, and the time-sharing dynamic water quality data and the time-sharing dynamic visualization data are combined as basic data to perform intelligent data analysis on suspicious clues such as whether there is environmental pollution in the fixed waters and the distance from the environmental pollution source to the fixed waters, thereby overcoming various defects of traditional pollution monitoring methods, enriching the analysis results of environmental pollution, and providing users with more auxiliary information for environmental pollution judgment and control.

[0007] According to the present invention, a method for automatically monitoring suspicious clues data analysis based on artificial intelligence is provided, the method comprising: Collecting water quality related data corresponding to the target monitoring water surface directly below the current monitoring point at each moment, wherein the water quality related data corresponding to the target monitoring water surface at each moment is the chemical oxygen demand, permanganate index, phosphorus content, total content of minerals dissolved in water, and water body temperature of the target monitoring water surface at the moment; Capturing visualization data of a real-time imaging picture corresponding to a target monitoring water surface directly below a current monitoring point, wherein the visualization data of the real-time imaging picture is each cyan component value, each magenta component value, each yellow component value, and each black component value corresponding to each pixel point of the real-time imaging picture; Performing a set number of learning actions on the convolutional neural network to obtain a convolutional neural network after completing each learning operation, and outputting the convolutional neural network after completing each learning operation as an automatic monitoring model for suspicious clues, wherein the number of learning operations completed by the convolutional neural network is positively correlated with the water surface area of ​​the target monitored water surface; The water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality related data corresponding to each time moment evenly spaced within the preset time interval, and the visualization data of each real-time imaging picture corresponding to each time moment evenly spaced within the preset time interval are synchronously input into the suspicious clue automatic monitoring model to run the suspicious clue automatic monitoring model to obtain the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model; Among them, the water quality related data of the target monitoring water surface directly below the current monitoring point originates from the water body at the target monitoring water surface directly below the current monitoring point; Among them, when the pollution mark output by the automatic monitoring model of suspicious clues indicates that the target monitored water surface is not a polluted water surface, the suspected distance from the pollution source to the current monitoring point output at the same time is zero.

[0008] Compared with the prior art, the present invention has at least the following important inventive features: Invention point A: For the target monitoring water surface directly below the current monitoring point, an artificial intelligence mechanism is used to intelligently analyze the pollution mark of whether the target monitoring water surface belongs to a polluted water surface and the suspected distance from the pollution source to the current monitoring point based on various time-sharing visual data of the target monitoring water surface and the time-sharing water quality related data of the target monitoring water surface, thereby completing the automatic monitoring, on-site display and wireless reporting of suspicious clue information of water surface environmental pollution including the pollution mark and the suspected distance from the pollution source to the current monitoring point based on the time-sharing dynamic information; Invention point B: The intelligent analysis of suspicious clue information is based on a suspicious clue automatic monitoring model, which is a convolutional neural network after completing each learning operation. It is particularly important that the number of learning operations completed by the convolutional neural network is positively correlated with the surface area of ​​the target monitored water surface, thereby constructing suspicious clue automatic monitoring models with different structures for monitored water surfaces of different areas, thereby ensuring the stability and effectiveness of the intelligent analysis results; Invention point C: Specifically, the various time-sharing visualization data of the target monitored water surface are visualization data of each real-time imaging screen corresponding to each moment evenly spaced within a preset time interval, each real-time imaging screen originates from a fixed overhead imaging mechanism at the current monitoring point, and the visualization data of each real-time imaging screen are each cyan component value, each magenta component value, each yellow component value and each black component value corresponding to each pixel point in the real-time imaging screen, and at the same time, the water quality-related data of the target monitored water surface at each moment are the chemical oxygen demand, permanganate index, phosphorus content, total content of minerals dissolved in water and water temperature of the water body at the target monitored water surface at the moment, and it is particularly critical that the number of each moment evenly spaced within the preset time interval is positively correlated with the water surface area of ​​the target monitored water surface, thereby selectively screening a number of comprehensive and sufficient basic information for the intelligent analysis of suspicious clue information, further ensuring the stability and effectiveness of the intelligent analysis results; Invention point D: In each learning operation performed on the convolutional neural network, a known pollution mark indicating whether a certain monitored water surface is a polluted water surface and the determined distance from the nearest pollution source near the certain monitored water surface to the corresponding monitoring point of the certain monitored water surface are used as two output contents of the convolutional neural network, and the water surface area of ​​the certain monitored water surface, the time length of a preset time interval, the various water quality-related data of the certain monitored water surface corresponding to each moment evenly spaced within the preset time interval, and the visualization data of each real-time imaging picture of the certain monitored water surface corresponding to each moment evenly spaced within the preset time interval are used as multiple input contents of the convolutional neural network to complete this learning operation, thereby ensuring the learning effect of each learning operation of the convolutional neural network. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1 A technical flow chart of the method for automatically monitoring data analysis of suspicious clues based on artificial intelligence according to the present invention; Figure 2 A flowchart of a method for automatically monitoring suspicious clues and analyzing data based on artificial intelligence according to Embodiment 1 of the present invention; Figure 3 A flowchart of a method for automatically monitoring suspicious clues data and analyzing it based on artificial intelligence according to Embodiment 2 of the present invention; Figure 4 A flowchart of a method for automatically monitoring suspicious clues data and analyzing it based on artificial intelligence according to Embodiment 3 of the present invention; Figure 5A flowchart of a method for automatically monitoring suspicious clues and analyzing data based on artificial intelligence according to Embodiment 4 of the present invention; Figure 6 The present invention is a flowchart showing a method for automatically monitoring suspicious clue data and analyzing it based on artificial intelligence according to Embodiment 5 of the present invention. DETAILED DESCRIPTION

[0010] like Figure 1 As shown, a technical flow chart of a method for automatically monitoring data and analyzing suspicious clues based on artificial intelligence according to the present invention is given.

[0011] like Figure 1 As shown, the specific technical process of the present invention is as follows: Technical process 1: For the target monitoring water surface directly below the current monitoring point, obtain various time-sharing visual data of the target monitoring water surface and time-sharing water quality related data of the target monitoring water surface, so as to provide sufficient and comprehensive basic data for the subsequent intelligent analysis of suspicious clues; For example, the suspicious clues obtained by the subsequent intelligent analysis include the pollution mark of whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point; Specifically, the various time-sharing visualization data of the target monitored water surface are visualization data of each real-time imaging screen corresponding to each moment evenly spaced within a preset time interval, each real-time imaging screen originates from a fixed overhead imaging mechanism at the current monitoring point, and the visualization data of each real-time imaging screen are each cyan component value, each magenta component value, each yellow component value and each black component value corresponding to each pixel point in the real-time imaging screen, and at the same time, the water quality-related data of the target monitored water surface at each moment are the chemical oxygen demand, permanganate index, phosphorus content, total content of minerals dissolved in water and water temperature of the water body at the target monitored water surface at the said moment, and it is particularly critical that the number of each moment evenly spaced within the preset time interval is positively correlated with the water surface area of ​​the target monitored water surface, thereby selectively screening a number of comprehensive and sufficient basic information for the intelligent analysis of suspicious clue information, further ensuring the stability and effectiveness of the intelligent analysis results; like Figure 1 As shown, the overhead camera mechanism at the current monitoring point adopts a fixing system including a vertical pole and a horizontal pole to fix the overhead camera mechanism; As well as Figure 1 As shown, the current monitoring point is also provided with a control box located on the vertical pole, a solar panel located on the horizontal pole, and a radar flow meter; In this way, through intelligent analysis of the changes in the acquired time-sharing dynamic data of the target monitored water surface, an intelligent and reliable judgment can be provided on the environmental pollution degree of the target monitored water surface and the distance of the pollution source; Technical process 2: Design a customized suspicious clue automatic monitoring model to perform subsequent intelligent analysis of suspicious clues; Specifically, the structural customization of the suspicious clue automatic monitoring model is mainly reflected in the following aspects: First, the automatic monitoring model for suspicious clues is a convolutional neural network after completing each learning operation; Second, the number of learning operations completed by the convolutional neural network is positively correlated with the surface area of ​​the target monitored water surface, thereby building suspicious clue automatic monitoring models with different structures for monitored water surfaces of different areas, ensuring the stability and effectiveness of the intelligent analysis results; Third, in each learning operation performed on the convolutional neural network, the known pollution mark indicating whether a certain monitored water surface is a polluted water surface and the determined distance from the nearest pollution source near the certain monitored water surface to the corresponding monitoring point of the certain monitored water surface are used as two output contents of the convolutional neural network, and the water surface area of ​​the certain monitored water surface, the time length of the preset time interval, the various water quality-related data of the certain monitored water surface corresponding to each time at even intervals within the preset time interval, and the visualization data of each real-time imaging picture of the certain monitored water surface corresponding to each time at even intervals within the preset time interval are used as multiple input contents of the convolutional neural network to complete this learning operation, thereby ensuring the learning effect of each learning operation of the convolutional neural network; Technical process three: The automatic monitoring model for suspicious clues designed in technical process two is used to intelligently analyze the pollution mark of the target monitoring water surface and the suspected distance from the pollution source to the current monitoring point based on the various time-sharing visual data of the target monitoring water surface selected in technical process one and the time-sharing water quality-related data of the target monitoring water surface; In this way, intelligent analysis and automatic monitoring of suspicious clue information of water surface environmental pollution based on time-sharing dynamic information, including pollution identification and suspected distance from the pollution source to the current monitoring point, are completed; Technical process 4: Display and wirelessly report suspicious clues obtained by intelligent analysis in technical process 3, including whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point; Specifically, a mobile communication interface at the current monitoring point is used to input the pollution mark indicating whether the target monitoring water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model into a network data packet, and then the network data packet is sent to a remote environmental monitoring server through a mobile communication link; Specifically, the on-site display screen at the current monitoring point is used to receive and display in real time the pollution mark output by the suspicious clue automatic monitoring model indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point.

[0012] The key points of the present invention are: targeted screening of dynamic basic data including various time-sharing visualization data of the target monitored water surface and time-sharing water quality-related data of the target monitored water surface, customized structural design of the automatic monitoring model of suspicious clues, and intelligent analysis of suspicious clues including pollution identification of whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point.

[0013] Below, the artificial intelligence-based suspicious clue automatic monitoring data analysis method of the present invention will be specifically described in the form of an embodiment.

[0014] Example 1 Figure 2 The present invention is a flowchart showing a method for automatically monitoring suspicious clue data and analyzing it based on artificial intelligence according to Embodiment 1 of the present invention.

[0015] like Figure 2 As shown, the method for automatically monitoring suspicious clues based on artificial intelligence includes the following specific steps: Step S201: collecting water quality related data corresponding to the target monitoring water surface directly below the current monitoring point at each moment, wherein the water quality related data corresponding to the target monitoring water surface at each moment is the chemical oxygen demand, permanganate index, phosphorus content, total content of minerals dissolved in water, and water temperature of the target monitoring water surface at the moment; Specifically, chemical oxygen demand is a measure of the amount of reducing substances in water, which is the amount of oxygen consumed by the oxidant when these substances react with strong oxidants under specific conditions. A high COD value indicates a high content of organic matter in the water, which may cause environmental pollution; Specifically, the permanganate index is calculated by the amount of potassium permanganate consumed to oxidize certain organic matter and inorganic reducing substances in the water sample, and it reflects the degree of pollution of the water by reducing substances; Specifically, the phosphorus content, i.e., total phosphorus, is the total content of phosphorus in water, including dissolved and particulate phosphorus. High phosphorus content may lead to eutrophication of water bodies, triggering algae proliferation and affecting water quality. And specifically, total dissolved solids is the sum of the mineral content dissolved in water. A high value of total dissolved solids means that the water contains more minerals, which may affect the taste and health of the water; Step S202: capturing visualization data of a real-time imaging picture corresponding to a target monitoring water surface directly below a current monitoring point, wherein the visualization data of the real-time imaging picture is each cyan component value, each magenta component value, each yellow component value, and each black component value corresponding to each pixel point of the real-time imaging picture; Step S203: performing a set number of learning actions on the convolutional neural network to obtain a convolutional neural network after completing each learning operation, and outputting the convolutional neural network after completing each learning operation as a suspicious clue automatic monitoring model, wherein the number of learning operations completed by the convolutional neural network is positively correlated with the water surface area of ​​the target monitored water surface; For example, the number of learning operations completed by the convolutional neural network is positively correlated with the water surface area of ​​the target monitored water surface, including: when the water surface area of ​​the target monitored water surface is 20 square meters, the number of learning operations completed by the convolutional neural network is 30 times, when the water surface area of ​​the target monitored water surface is 50 square meters, the number of learning operations completed by the convolutional neural network is 50 times, when the water surface area of ​​the target monitored water surface is 100 square meters, the number of learning operations completed by the convolutional neural network is 70 times, and so on; Step S204: synchronously inputting the water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality related data corresponding to each time moment evenly spaced within the preset time interval, and the visualization data of each real-time imaging screen corresponding to each time moment evenly spaced within the preset time interval into the suspicious clue automatic monitoring model, so as to run the suspicious clue automatic monitoring model and obtain the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model; For example, the water surface area of ​​the target monitored water surface, the time length of the preset time interval, each portion of water quality related data corresponding to each moment evenly spaced within the preset time interval, and each portion of visual data of real-time imaging pictures corresponding to each moment evenly spaced within the preset time interval are synchronously input into the suspicious clue automatic monitoring model to run the suspicious clue automatic monitoring model, and obtain the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model, including: the preset time interval is the time interval between 9:00 a.m. and 10:00 a.m. of the same day, and each moment evenly spaced within the preset time interval is the time interval between 9:00 a.m. and 10:00 a.m. of the same day, which is a 10-minute time point, namely, 9:10 a.m. of the same day, 9:20 a.m. of the same day, 9:30 a.m. of the same day, 9:40 a.m. of the same day, 9:50 a.m. of the same day, and 10:00 a.m. of the same day; Among them, the water quality related data of the target monitoring water surface directly below the current monitoring point originates from the water body at the target monitoring water surface directly below the current monitoring point; Specifically, the water quality related data of the target monitoring water surface directly below the current monitoring point are derived from the water body at the target monitoring water surface directly below the current monitoring point, including: the water quality related data of the target monitoring water surface directly below the current monitoring point are all taken from the water body at the target monitoring water surface directly below the current monitoring point; Wherein, when the pollution mark output by the suspicious clue automatic monitoring model indicates that the target monitored water surface is not a polluted water surface, the suspected distance outputted from the pollution source to the current monitoring point is zero; In this way, when the pollution mark output by the suspicious clue automatic monitoring model indicates that the target monitored water surface is a polluted water surface, the suspected distance from the pollution source to the current monitoring point output at the same time is the actual distance from the pollution source to the current monitoring point that needs to be confirmed later; Among them, the convolutional neural network is subjected to a set number of learning actions to obtain a convolutional neural network after each learning operation is completed, and the convolutional neural network after each learning operation is output as a suspicious clue automatic monitoring model, and the number of learning operations completed by the convolutional neural network is positively correlated with the water surface area of ​​the target monitored water surface, including: in each learning operation performed on the convolutional neural network, a known pollution mark indicating whether a certain monitored water surface is a polluted water surface and a determined distance from the nearest pollution source near the certain monitored water surface to the corresponding monitoring point of the certain monitored water surface are used as two output contents of the convolutional neural network, and the water surface area of ​​the certain monitored water surface, the time length of the preset time interval, each portion of water quality related data of the certain monitored water surface corresponding to each time evenly spaced in the preset time interval, and each portion of visualization data of real-time imaging pictures of the certain monitored water surface corresponding to each time evenly spaced in the preset time interval are used as multiple input contents of the convolutional neural network to complete this learning operation; Wherein, the water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality related data corresponding to each time moment evenly spaced within the preset time interval, and the visualization data of each real-time imaging screen corresponding to each time moment evenly spaced within the preset time interval are synchronously input into the suspicious clue automatic monitoring model, including: the number of each time moment evenly spaced within the preset time interval is positively correlated with the water surface area of ​​the target monitored water surface; For example, the number of evenly spaced moments within a preset time interval is positively correlated with the water surface area of ​​the target monitored water surface, including: the water surface area of ​​the target monitored water surface is 20 square meters, the number of evenly spaced moments within the preset time interval is 6, the water surface area of ​​the target monitored water surface is 50 square meters, the number of evenly spaced moments within the preset time interval is 12, the water surface area of ​​the target monitored water surface is 100 square meters, the number of evenly spaced moments within the preset time interval is 18, and so on; Wherein, the water quality related data corresponding to each moment of the target monitoring water surface directly below the current monitoring point is collected, and the water quality related data corresponding to each moment of the target monitoring water surface is the chemical oxygen demand, permanganate index, phosphorus content, the total content of minerals dissolved in water and water body temperature of the target monitoring water surface at the moment, including: the permanganate index of the target monitoring water surface indicates the degree of water pollution by reducing substances at the target monitoring water surface; Wherein, the visualization data of the real-time imaging picture corresponding to the target monitoring water surface directly below the current monitoring point is captured, and the visualization data of the real-time imaging picture is each cyan component value, each magenta component value, each yellow component value and each black component value respectively corresponding to each pixel point of the real-time imaging picture, including: the real-time imaging picture corresponding to the target monitoring water surface directly below the current monitoring point is the real-time picture taken by the overhead imaging mechanism fixed at the current monitoring point; And wherein, the visualization data of the real-time imaging picture corresponding to the target monitoring water surface directly below the current monitoring point is captured, and the visualization data of the real-time imaging picture is each cyan component value, each magenta component value, each yellow component value and each black component value corresponding to each pixel point of the real-time imaging picture, including: the cyan component value, magenta component value, yellow component value and black component value corresponding to each pixel point of the real-time imaging picture are the C component value, M component value, Y component value and K component value of the pixel point in the CMYK color space.

[0016] Example 2 Figure 3 The present invention is a flowchart showing a method for automatically monitoring suspicious clue data and analyzing it based on artificial intelligence according to Embodiment 2 of the present invention.

[0017] like Figure 3 As shown, Figure 2Different from the embodiment in, before capturing the visualization data of the real-time imaging picture corresponding to the target monitoring water surface directly below the current monitoring point, the visualization data of the real-time imaging picture being each cyan component value, each magenta component value, each yellow component value and each black component value corresponding to each pixel point of the real-time imaging picture, that is, before step S202, the method further includes: Step S301: using a fixed overhead imaging mechanism at the current monitoring point to perform an overhead imaging operation on the target monitoring water surface to obtain and output a corresponding real-time imaging picture; Specifically, a fixed overhead camera mechanism at the current monitoring point is used to perform an overhead camera operation on the target monitored water surface to obtain and output a corresponding real-time imaging picture, including: the overhead camera mechanism includes a CMOS sensor, an overhead camera lens, a flexible circuit board and a filter.

[0018] Example 3 Figure 4 The present invention is a flowchart showing a method for automatically monitoring suspicious clue data and analyzing it based on artificial intelligence according to Embodiment 3 of the present invention.

[0019] like Figure 4 As shown, Figure 2 Different from the embodiment in, after the surface area of ​​the target monitored water surface, the time length of the preset time interval, the respective water quality related data corresponding to the respective moments evenly spaced within the preset time interval, and the visual data of the respective real-time imaging pictures corresponding to the respective moments evenly spaced within the preset time interval are synchronously input into the suspicious clue automatic monitoring model to run the suspicious clue automatic monitoring model, and obtain the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model, that is, after step S204, the method further includes: Step S401: using the mobile communication interface at the current monitoring point to input the pollution mark indicating whether the target monitoring water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model into a network data packet, and then sending the network data packet to a remote environmental monitoring server through a mobile communication link; Specifically, the mobile communication interface at the current monitoring point is used to input the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model into a network data packet, and then the network data packet is sent to a remote environmental monitoring server through a mobile communication link, including: the environmental monitoring server is one of a big data monitoring network element, a cloud computing monitoring network element or a blockchain monitoring network element.

[0020] Example 4 Figure 5 The present invention is a flowchart showing a method for automatically monitoring suspicious clue data and analyzing it based on artificial intelligence according to Embodiment 4 of the present invention.

[0021] like Figure 5 As shown, Figure 2 Different from the embodiment in, after the surface area of ​​the target monitored water surface, the time length of the preset time interval, the respective water quality related data corresponding to the respective moments evenly spaced within the preset time interval, and the visual data of the respective real-time imaging pictures corresponding to the respective moments evenly spaced within the preset time interval are synchronously input into the suspicious clue automatic monitoring model to run the suspicious clue automatic monitoring model, and obtain the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model, that is, after step S204, the method further includes: Step S501: Using the on-site display screen at the current monitoring point to receive and display in real time the pollution mark output by the suspicious clue automatic monitoring model indicating whether the target monitoring water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point Specifically, the on-site display screen at the current monitoring point is used to receive and display in real time the pollution mark output by the suspicious clue automatic monitoring model indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point, including: the on-site display screen is an LCD display array or an LED display array.

[0022] Example 5 Figure 6 The present invention is a flowchart showing a method for automatically monitoring suspicious clue data and analyzing it based on artificial intelligence according to Embodiment 5 of the present invention.

[0023] like Figure 6 As shown, Figure 2 Different from the embodiment in, after performing a set number of learning actions on the convolutional neural network to obtain a convolutional neural network after completing each learning operation, and outputting the convolutional neural network after completing each learning operation as a suspicious clue automatic monitoring model, and the number of learning operations completed by the convolutional neural network is positively correlated with the water surface area of ​​the target monitored water surface, that is, after step S203, the method further includes: Step S601: storing various model parameters of the suspicious clue automatic monitoring model to achieve model storage of the suspicious clue automatic monitoring model; For example, storing various model parameters of the automatic monitoring model for suspicious clues to achieve model storage of the automatic monitoring model for suspicious clues includes: selecting an SD storage device, a TF storage device or an MMC storage device to store various model parameters of the automatic monitoring model for suspicious clues to achieve model storage of the automatic monitoring model for suspicious clues.

[0024] Next, various method embodiments of the present invention are described in detail.

[0025] In the suspicious clue automatic monitoring data analysis method based on artificial intelligence according to various method embodiments of the present invention: The water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality related data corresponding to each moment evenly spaced within the preset time interval, and the visual data of each real-time imaging screen corresponding to each moment evenly spaced within the preset time interval are synchronously input into the suspicious clue automatic monitoring model to run the suspicious clue automatic monitoring model, and obtain the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model, including: performing binary value conversion on the water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality related data corresponding to each moment evenly spaced within the preset time interval, and the visual data of each real-time imaging screen corresponding to each moment evenly spaced within the preset time interval, and then synchronously inputting them into the suspicious clue automatic monitoring model; Specifically, the water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality related data corresponding to each time moment evenly spaced within the preset time interval, and the visual data of each real-time imaging picture corresponding to each time moment evenly spaced within the preset time interval are converted into binary values ​​and then synchronously input into the automatic monitoring model for suspicious clues, including: selecting a synchronous driving mechanism implemented by a programmable logic device to implement the synchronous input; The water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality-related data corresponding to each moment evenly spaced within the preset time interval, and the visualization data of each real-time imaging picture corresponding to each moment evenly spaced within the preset time interval are synchronously input into the suspicious clue automatic monitoring model to run the suspicious clue automatic monitoring model, and obtain the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model. It also includes: the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model are both in the form of binary numerical values.

[0026] In the suspicious clue automatic monitoring data analysis method based on artificial intelligence according to various method embodiments of the present invention: Performing a set number of learning actions on the convolutional neural network to obtain a convolutional neural network after completing each learning operation, and outputting the convolutional neural network after completing each learning operation as a suspicious clue automatic monitoring model, wherein the number of learning operations completed by the convolutional neural network is positively correlated with the water surface area of ​​the target monitored water surface, including: using a numerical mapping formula to represent a numerical mapping relationship of the number of learning operations completed by the convolutional neural network and the water surface area of ​​the target monitored water surface; Specifically, the MATLAB toolbox may be selected to implement the testing and simulation of the data processing process of using a numerical mapping formula to represent the numerical mapping relationship of the number of learning operations completed by the convolutional neural network and the positive correlation between the water surface area of ​​the target monitored water surface; Among them, the numerical mapping relationship in which the number of learning operations completed by the convolutional neural network and the water surface area of ​​the target monitored water surface are expressed by a numerical mapping formula includes: in the numerical mapping formula, the water surface area of ​​the target monitored water surface is the input value of the numerical mapping formula, and the number of learning operations completed by the convolutional neural network corresponding to the water surface area of ​​the target monitored water surface is the output value of the numerical mapping formula.

[0027] And in the suspicious clue automatic monitoring data analysis method based on artificial intelligence according to each method embodiment of the present invention: The cyan component value, magenta component value, yellow component value and black component value corresponding to each pixel point of the real-time imaging picture are the C component value, M component value, Y component value and K component value of the pixel point in the CMYK color space, including: the value range of any color component value among the cyan component value, magenta component value, yellow component value and black component value corresponding to each pixel point of the real-time imaging picture is between 0-255.

[0028] In addition, the present invention may also cite the following technical contents to highlight the significant technical progress of the present invention: Collecting water quality related data corresponding to each moment of the target monitoring water surface directly below the current monitoring point, wherein the water quality related data corresponding to each moment of the target monitoring water surface is the chemical oxygen demand, permanganate index, phosphorus content, the total content of minerals dissolved in water, and the water body temperature of the target monitoring water surface at the moment, further comprising: using a plurality of real-time measurement components arranged at the current monitoring point, and respectively measuring the chemical oxygen demand, permanganate index, phosphorus content, the total content of minerals dissolved in water, and the water body temperature of the target monitoring water surface corresponding to the moment when arriving at each moment; Specifically, a plurality of real-time measurement components are arranged at the current monitoring point, and when each moment arrives, they are used to respectively measure the chemical oxygen demand, permanganate index, phosphorus content, the total content of minerals dissolved in water, and the water body temperature of the target monitored water surface corresponding to the moment, including: a timing service component and a synchronous drive component are also arranged at the current monitoring point, the timing service component is used to determine whether each moment has arrived, and the synchronous drive component is connected to the timing service component and is also connected to the plurality of real-time measurement components, and is used to drive the plurality of real-time measurement components to respectively measure the chemical oxygen demand, permanganate index, phosphorus content, the total content of minerals dissolved in water, and the water body temperature of the target monitored water surface corresponding to the moment when each moment arrives; And wherein, the visualization data of the real-time imaging picture corresponding to the target monitoring water surface directly below the current monitoring point is captured, and the visualization data of the real-time imaging picture is each cyan component value, each magenta component value, each yellow component value and each black component value corresponding to each pixel point of the real-time imaging picture, and also includes: keeping the overhead lens of the overhead imaging mechanism fixed at the current monitoring point within the horizontal plane.

[0029] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A suspicious clue automatic monitoring data analysis method based on artificial intelligence, characterized in that: The method comprises: Collecting water quality related data corresponding to the target monitoring water surface directly below the current monitoring point at each moment, wherein the water quality related data corresponding to the target monitoring water surface at each moment is the chemical oxygen demand, permanganate index, phosphorus content, total content of minerals dissolved in water, and water body temperature of the target monitoring water surface at the moment; Capturing visualization data of a real-time imaging picture corresponding to a target monitoring water surface directly below a current monitoring point, wherein the visualization data of the real-time imaging picture is each cyan component value, each magenta component value, each yellow component value, and each black component value corresponding to each pixel point of the real-time imaging picture; Performing a set number of learning actions on the convolutional neural network to obtain a convolutional neural network after completing each learning operation, and outputting the convolutional neural network after completing each learning operation as an automatic monitoring model for suspicious clues, wherein the number of learning operations completed by the convolutional neural network is positively correlated with the water surface area of ​​the target monitored water surface; The water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality related data corresponding to each time moment evenly spaced within the preset time interval, and the visualization data of each real-time imaging picture corresponding to each time moment evenly spaced within the preset time interval are synchronously input into the suspicious clue automatic monitoring model to run the suspicious clue automatic monitoring model to obtain the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model; Among them, the water quality related data of the target monitoring water surface directly below the current monitoring point originates from the water body at the target monitoring water surface directly below the current monitoring point; Among them, when the pollution mark output by the automatic monitoring model of suspicious clues indicates that the target monitored water surface is not a polluted water surface, the suspected distance from the pollution source to the current monitoring point output at the same time is zero.

2. The method for automatically monitoring suspicious clues based on artificial intelligence as claimed in claim 1, characterized in that: Performing a set number of learning actions on the convolutional neural network to obtain a convolutional neural network after completing each learning operation, and outputting the convolutional neural network after completing each learning operation as a suspicious clue automatic monitoring model, wherein the number of learning operations completed by the convolutional neural network is positively correlated with the water surface area of ​​the target monitored water surface, including: in each learning operation performed on the convolutional neural network, using a known pollution mark indicating whether a certain monitored water surface is a polluted water surface and a determined distance from the nearest pollution source near the certain monitored water surface to a corresponding monitoring point of the certain monitored water surface as two output contents of the convolutional neural network, using the water surface area of ​​the certain monitored water surface, the time length of a preset time interval, each portion of water quality-related data of the certain monitored water surface corresponding to each evenly spaced moment in the preset time interval, and each portion of visualization data of real-time imaging pictures of the certain monitored water surface corresponding to each evenly spaced moment in the preset time interval as multiple input contents of the convolutional neural network to complete this learning operation; Among them, the water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality-related data corresponding to each moment evenly spaced within the preset time interval, and the visualization data of each real-time imaging picture corresponding to each moment evenly spaced within the preset time interval are synchronously input into the automatic monitoring model of suspicious clues, including: the number of each moment evenly spaced within the preset time interval is positively correlated with the water surface area of ​​the target monitored water surface.

3. The method for automatically monitoring suspicious clues based on artificial intelligence as claimed in claim 2, characterized in that: Collecting water quality related data corresponding to the target monitoring water surface directly below the current monitoring point at each moment, wherein the water quality related data corresponding to the target monitoring water surface at each moment are the chemical oxygen demand, permanganate index, phosphorus content, the total content of minerals dissolved in water, and water body temperature of the target monitoring water surface at the moment, including: the permanganate index of the target monitoring water surface indicates the degree of water pollution by reducing substances at the target monitoring water surface; Wherein, the visualization data of the real-time imaging picture corresponding to the target monitoring water surface directly below the current monitoring point is captured, and the visualization data of the real-time imaging picture is each cyan component value, each magenta component value, each yellow component value and each black component value respectively corresponding to each pixel point of the real-time imaging picture, including: the real-time imaging picture corresponding to the target monitoring water surface directly below the current monitoring point is the real-time picture taken by the overhead imaging mechanism fixed at the current monitoring point; Among them, the visualization data of the real-time imaging picture corresponding to the target monitoring water surface directly below the current monitoring point is captured, and the visualization data of the real-time imaging picture is each cyan component value, each magenta component value, each yellow component value and each black component value corresponding to each pixel point of the real-time imaging picture, including: the cyan component value, magenta component value, yellow component value and black component value corresponding to each pixel point of the real-time imaging picture are the C component value, M component value, Y component value and K component value of the pixel point in the CMYK color space.

4. The method for automatically monitoring suspicious clues based on artificial intelligence as claimed in claim 3 is characterized in that: Before capturing visualization data of a real-time imaging picture corresponding to a target monitoring water surface directly below a current monitoring point, wherein the visualization data of the real-time imaging picture is each cyan component value, each magenta component value, each yellow component value, and each black component value corresponding to each pixel point of the real-time imaging picture, the method further includes: A fixed overhead camera mechanism at the current monitoring point is used to perform an overhead camera operation on the target monitoring water surface to obtain and output the corresponding real-time imaging picture.

5. The method for automatically monitoring suspicious clues based on artificial intelligence as claimed in claim 3 is characterized in that: After synchronously inputting the water surface area of ​​the target monitored water surface, the time length of the preset time interval, the respective water quality related data corresponding to the respective moments evenly spaced within the preset time interval, and the respective visualization data of the respective real-time imaging pictures corresponding to the respective moments evenly spaced within the preset time interval into the suspicious clue automatic monitoring model to run the suspicious clue automatic monitoring model, and obtaining the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model, the method further includes: The mobile communication interface at the current monitoring point is used to input the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model into a network data packet, and then the network data packet is sent to a remote environmental monitoring server through a mobile communication link.

6. The method for automatically monitoring suspicious clues based on artificial intelligence as claimed in claim 3 is characterized in that: After synchronously inputting the water surface area of ​​the target monitored water surface, the time length of the preset time interval, the respective water quality related data corresponding to the respective moments evenly spaced within the preset time interval, and the respective visualization data of the respective real-time imaging pictures corresponding to the respective moments evenly spaced within the preset time interval into the suspicious clue automatic monitoring model to run the suspicious clue automatic monitoring model, and obtaining the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model, the method further includes: The on-site display screen at the current monitoring point is used to receive and display in real time the pollution mark output by the suspicious clue automatic monitoring model indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point.

7. The method for automatically monitoring suspicious clues based on artificial intelligence as claimed in claim 3 is characterized in that: After performing a set number of learning actions on the convolutional neural network to obtain a convolutional neural network after completing each learning operation, and outputting the convolutional neural network after completing each learning operation as a suspicious clue automatic monitoring model, and the number of learning operations completed by the convolutional neural network is positively correlated with the water surface area of ​​the target monitored water surface, the method further includes: The model storage of the suspicious clue automatic monitoring model is realized by storing various model parameters of the suspicious clue automatic monitoring model.

8. The method for automatically monitoring suspicious clues based on artificial intelligence as claimed in any one of claims 3 to 7, characterized in that: The water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality related data corresponding to each moment evenly spaced within the preset time interval, and the visual data of each real-time imaging screen corresponding to each moment evenly spaced within the preset time interval are synchronously input into the suspicious clue automatic monitoring model to run the suspicious clue automatic monitoring model, and obtain the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model, including: performing binary value conversion on the water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality related data corresponding to each moment evenly spaced within the preset time interval, and the visual data of each real-time imaging screen corresponding to each moment evenly spaced within the preset time interval, and then synchronously inputting them into the suspicious clue automatic monitoring model; The water surface area of ​​the target monitored water surface, the time length of the preset time interval, the water quality-related data corresponding to each moment evenly spaced within the preset time interval, and the visualization data of each real-time imaging picture corresponding to each moment evenly spaced within the preset time interval are synchronously input into the suspicious clue automatic monitoring model to run the suspicious clue automatic monitoring model, and obtain the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model. It also includes: the pollution mark indicating whether the target monitored water surface is a polluted water surface and the suspected distance from the pollution source to the current monitoring point output by the suspicious clue automatic monitoring model are both in the form of binary numerical values.

9. The method for automatically monitoring suspicious clues based on artificial intelligence as claimed in any one of claims 3 to 7, characterized in that: Performing a set number of learning actions on the convolutional neural network to obtain a convolutional neural network after completing each learning operation, and outputting the convolutional neural network after completing each learning operation as a suspicious clue automatic monitoring model, wherein the number of learning operations completed by the convolutional neural network is positively correlated with the water surface area of ​​the target monitored water surface, including: using a numerical mapping formula to represent a numerical mapping relationship of the number of learning operations completed by the convolutional neural network and the water surface area of ​​the target monitored water surface; Among them, the numerical mapping relationship in which the number of learning operations completed by the convolutional neural network and the water surface area of ​​the target monitored water surface are expressed by a numerical mapping formula includes: in the numerical mapping formula, the water surface area of ​​the target monitored water surface is the input value of the numerical mapping formula, and the number of learning operations completed by the convolutional neural network corresponding to the water surface area of ​​the target monitored water surface is the output value of the numerical mapping formula.

10. The method for automatically monitoring suspicious clues based on artificial intelligence according to any one of claims 3 to 7, characterized in that: The cyan component value, magenta component value, yellow component value and black component value corresponding to each pixel point of the real-time imaging picture are the C component value, M component value, Y component value and K component value of the pixel point in the CMYK color space, including: the value range of any color component value among the cyan component value, magenta component value, yellow component value and black component value corresponding to each pixel point of the real-time imaging picture is between 0-255.

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