Industrial internet oriented water pollution intelligent early warning and collaborative management system

Abstract

The application relates to the technical field of water pollution supervision, and discloses an intelligent early warning and collaborative management system for water pollution facing an industrial internet, which efficiently captures water body abnormal features in a water body area through a cloud lens carried by a drone; based on water body visual feature image data and in combination with an artificial intelligence algorithm and standard stored water body water pollution standard visual abnormal image data, intelligent analysis is conducted on a visual abnormal state of a target water body area, global scientific analysis of a water body water pollution state is realized based on a water body visual level, a smell detector is carried by the drone to dynamically capture smell abnormal features in the water body area; according to water body smell information and in combination with an intelligent search algorithm and water body water pollution standard smell abnormal information stored based on big data, efficient identification of a smell abnormal state of the target water body area is realized, efficient identification of the water body water pollution state based on smell detection is realized, and the applicability and convenience of water body water pollution early warning and supervision are improved.

Description

Technical Field

[0001] This invention relates to the technical field of water pollution monitoring, specifically to a smart early warning and collaborative management system for water pollution oriented towards the industrial internet. Background Technology

[0002] The water pollution early warning system based on the Industrial Internet of Things (IIoT) addresses the bottlenecks of traditional water environment monitoring models in terms of coverage, real-time performance, source tracing capabilities, and response efficiency. The core of this system relies on a sensing layer composed of multi-parameter water quality sensors, achieving efficient data transmission through wireless communication technologies such as 4G / 5G and NB-IoT. Cloud or edge platforms integrate big data analytics and artificial intelligence algorithms to process real-time water data, enabling early warning and source tracing of pollution events. By constructing a closed loop of "sensing-transmission-analysis-application," the system incorporates multiple stakeholders, including government, enterprises, and the public, into a collaborative regulatory network, supporting intelligent control throughout the entire process from water sources to discharge outlets. This promotes a shift in water environment management from reactive post-event response to proactive early warning, precise source tracing, and collaborative action. Existing water pollution early warning systems, relying solely on water quality monitoring, cannot achieve remote, safe, flexible, intelligent, and efficient early warning of sudden water pollution events, thus reducing the efficiency and applicability of water pollution early warning and supervision.

[0003] Chinese invention patent application CN115774953B, published on April 26, 2024, discloses a data processing-based spatiotemporal risk monitoring and assessment system for pollution. This system includes a farmland non-point source pollution monitoring system, a comprehensive farmland non-point source pollution risk index assessment system, and a spatiotemporal dynamic risk display module. It can perform three-level monitoring of the confluence nodes, sources, and receiving water bodies of farmland in a target area throughout its entire lifecycle, obtaining comprehensive water environment parameters. Based on these parameters, it can then perform real-time assessment and prediction of farmland non-point source pollution risks in the target area and classify corresponding risk levels. Simultaneously, the spatiotemporal dynamic risk display module provides pollution risk alarms or warnings for medium- and high-risk farmland non-point source confluence nodes or farmland, helping regulators intuitively grasp the distribution of farmland non-point source pollution risks and facilitating timely intervention and control. This provides technical support for determining "where to reduce" and "how to manage" farmland non-point source pollution. However, the above technical solution, which monitors farmland water pollution through farmland water environment parameters, is not only inflexible in practical applications but also unsuitable for remote monitoring of farmland water pollution status. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing water pollution early warning systems, which rely on water quality monitoring to provide remote, safe, flexible, intelligent, and efficient early warnings for sudden water pollution events, thus reducing the efficiency and applicability of water pollution early warning and supervision, this system aims to achieve the following: precise location of target water bodies; autonomous remote execution of water pollution supervision operations; real-time acquisition of visual feature images of target water bodies; intelligent analysis of visual anomalies based on visual detection; accurate capture of odor information from target water bodies; accurate identification of odor anomalies based on odor detection; and remote intelligent early warning of water pollution.

[0005] (II) Technical Solution This invention is achieved through the following technical solution: a smart early warning and collaborative management system for water pollution oriented towards the industrial internet, the system comprising the following: The water pollution monitoring and control module is used to collect feature information of the target water body area, perform geographic location coordinate positioning processing of the target water body area based on the feature information of the water body area, obtain the location coordinate data of the target water body area, and perform water pollution monitoring operations of the target water body. The water pollution visual odor detection module is used to collect visual feature image data of the target water body, analyze and process the visual anomaly state of the target water body area to obtain visual anomaly analysis information of the target water body; collect odor feature information of the target water body; and identify and process the odor anomaly state of the target water body area to obtain odor anomaly identification information of the target water body. The water pollution assessment and early warning module performs a comprehensive assessment of the water pollution status of the target water body based on the visual and odor detection results of the water body area, and obtains the water pollution status assessment information of the target water body; when the status is normal, the current water pollution monitoring operation of the target water body ends; when the status is abnormal, the water pollution early warning operation of the target water body is executed. The water pollution monitoring and control module includes a water body area information acquisition unit, a water body area location coordinate positioning unit, and a water body water pollution monitoring operation execution unit. The water body area information acquisition unit collects target water body area feature information through the data input dialog box of the water pollution industrial internet platform; the water body area location coordinate positioning unit performs geographic location coordinate positioning processing of the target water body area based on the target water body area feature information and in conjunction with the GIS system to obtain target water body area location coordinate data; the water body pollution monitoring operation execution unit performs target water body pollution monitoring operations based on the target water body area location coordinate data and in conjunction with a drone carrying water pollution detection equipment. The water pollution visual odor detection module includes a water body visual feature image acquisition unit, a water body water pollution standard visual anomaly image information storage unit, a water body visual anomaly analysis unit, a water body odor information acquisition unit, a water body water pollution standard odor anomaly information storage unit, and a water body odor anomaly identification unit. The water body visual feature image acquisition unit acquires water body visual feature image data via a drone equipped with a cloud lens; the water body water pollution standard visual anomaly image information storage unit stores water body water pollution standard visual anomaly image data; the water body visual anomaly analysis unit performs visual anomaly state analysis processing on the target water body area based on the water body visual feature image data and the water body water pollution standard visual anomaly image data to obtain target water body visual anomaly analysis information; the water body odor information acquisition unit acquires water body odor information via a drone equipped with an odor detector; the water body water pollution standard odor anomaly information storage unit stores water body water pollution standard odor anomaly information; the water body odor anomaly identification unit performs odor anomaly state identification processing on the target water body area based on the water body odor information and the water body water pollution standard odor anomaly information to obtain target water body odor anomaly identification information. The water pollution assessment and early warning module includes a comprehensive water pollution assessment unit and a water pollution detection and early warning unit. The comprehensive water pollution judgment unit performs a comprehensive judgment on the water pollution status of the target water body area based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, and obtains the water pollution status judgment information of the target water body; the water pollution detection and early warning unit performs the target water pollution early warning operation based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, combined with the water pollution industrial internet platform and the display screen.

[0006] Preferably, the steps for collecting target water body area feature information, performing geographic location coordinate positioning processing based on the water body area feature information, obtaining target water body area location coordinate data, and executing target water body water pollution monitoring operations are as follows: The geographical name and geographical number of the target water body area are collected online through the data input dialog box of the water pollution industrial internet platform, and the characteristic information of the target water body area is obtained; the water pollution industrial internet platform includes any one of Yanyun Smart Water Industrial Internet Platform, Yukong Water Quality Online Monitoring Cloud Platform, and HanClouds Environmental Monitoring Cloud. Based on the feature information of the target water body area, the geographic location coordinates of the target water body area are processed to obtain the target water body area location coordinate dataset, which is then merged and used for water pollution monitoring operations of the target water body.

[0007] Preferably, the steps for performing geographic location coordinate positioning processing of the target water body area based on the target water body area feature information, and merging the target water body area location coordinate dataset to execute the target water body water pollution monitoring operation are as follows: Using a GIS system, based on the geographic name and geographic number information corresponding to the feature information of the target water body area, the geographical coordinate information of the target water body area is searched in a targeted manner, and a set of location coordinate data of the target water body area is constructed. ,in This indicates the first search result for the target water body area. Location coordinate data of a target water body area, wherein the location coordinate data of the target water body area includes the longitude, latitude and altitude of the target water body area; Using a drone equipped with water pollution detection equipment, the data is based on the location coordinates of the target water body area. The target water body area location coordinate data described in the document The system performs remote water pollution detection and monitoring of target water bodies, and the water pollution detection equipment includes a cloud camera and an odor detector.

[0008] Preferably, the steps for collecting visual feature image data of the target water body, analyzing and processing the visual anomaly state of the target water body area to obtain visual anomaly analysis information of the target water body; collecting odor feature information of the target water body; and identifying and processing the odor anomaly state of the target water body area to obtain odor anomaly identification information of the target water body are as follows: The visual anomaly state analysis of the target water body area is performed by collecting a set of visual feature images of water bodies and a matrix of standard visual anomaly images of water pollution, and obtaining visual anomaly analysis information of the target water body. The odor information set of collected water bodies and the odor anomaly information matrix of water pollution standards are used to identify the odor anomaly status of the target water body area, and the odor anomaly identification information of the target water body is obtained.

[0009] Preferably, the steps for collecting a set of visual feature images of water bodies and a matrix of standard visual anomaly images of water pollution to analyze the visual anomaly status of the target water body area and obtain visual anomaly analysis information of the target water body are as follows: During water pollution monitoring operations in target water bodies, drones equipped with cloud cameras are used to capture online images of the water body's appearance, and a set of visual feature image data of the water body is constructed. ,in Indicates the number of collections Visual feature image data of water bodies; Establish a standard visual anomaly image data matrix for water pollution. ,in Indicates the first The standard visual anomaly image data of water pollution corresponding to different types of visual anomalies in water bodies; wherein the types of visual anomalies in water pollution include black water bodies, red water bodies, green water bodies, brown water bodies, turbid water bodies, and floating dead aquatic animals or animals in the water bodies; the standard visual anomaly image data of water pollution represents the standard water body appearance image data set for different types of visual anomalies in water pollution. The set of visual feature images of the water body The water body visual feature image data described in the document With the water body water pollution standard visual anomaly image data matrix The water pollution standard visual anomaly image data described in the document The specific steps for generating visual anomaly analysis information of the target water body are as follows: Water body visual image feature matching is performed, and visual anomaly analysis information of the target water body is generated based on the matching results. Step 2131, Initialization Phase: In the water pollution standard visual anomaly image data matrix... Within the search space, a set of initial positions of individual zebras for water body visual analysis are randomly generated, and the positions of these zebras are calculated in the water pollution standard visual anomaly image data matrix. The fitness values ​​in the search space are updated and the maximum number of iterations is updated. The formula for calculating the initial position of an individual zebra in the water body visual analysis is as follows: ,in Indicates the first Visual analysis of individual zebras in water bodies, in dimension [missing information]. The water pollution standard visual anomaly image data matrix The initial position in the search space. and These represent the visual anomaly image data matrix of zebra individuals in the water body according to the water pollution standard. The upper and lower limits of the search range within the search space. Indicates the value The random number; where the fitness value is calculated using the following formula: ,in Indicates spatial location as Visual analysis of individual zebras in water bodies The water pollution standard visual anomaly image data matrix The fitness value in the search space. The objective function represents the fitness value; Step 2132, Foraging Behavior Stage, in the water pollution standard visual anomaly image data matrix The individual zebra with the highest fitness value in the zebra population for water body visual analysis within the search space is considered the pioneer zebra for water body visual analysis; the pioneer zebra guides other members of the water body visual analysis zebra population in the water pollution standard visual anomaly image data matrix. The search space is used to move and update to a better position, and the position update formula is as follows: ,in Indicates the first Visual analysis of individual zebras in water bodies, in dimension [missing information]. The water pollution standard visual anomaly image data matrix The update position in the search space; This indicates that the pioneering zebra individuals in water body visual analysis are in dimension [missing information]. The water pollution standard visual anomaly image data matrix The update position in the search space; This represents the control factor controlling individual changes in the zebra population under visual analysis of water bodies, and takes the value of 1 or 2. Step 2133, Defense Strategy Selection Stage: The water body visual analysis zebra uses the standard visual anomaly image data matrix of water pollution in the water body. The type of defense strategy is determined based on the type of predator detected in the search space; the types of defense strategies include defense against lion attacks and defense against other predators. Step 21331, Lion Attack and Defense Phase: When the visual analysis of individual zebras in the water body is performed on the water pollution standard visual anomaly image data matrix... When a lion is detected as a predator in the search space, the visual analysis of zebra individuals in the water body is performed according to the visual anomaly image data matrix of the water pollution standard. Search the search space for image data matching the visual features of the water body. Matching the standard visual anomaly image data of water pollution in the water body During the process, the location is randomly moved and changed. The formula for calculating the random movement and change of location is as follows: ,in This indicates the first phase of the lion's attack and defense phase. Visual analysis of individual zebras in water bodies, in dimension [missing information]. The water pollution standard visual anomaly image data matrix Random movement and relocation within the search space; Step 21332, Other Predator Defense Phase: When the visual analysis of individual zebras in the water body is performed on the water pollution standard visual anomaly image data matrix... When a predator of type other is detected in the search space, the zebra individual in the water body is analyzed using the water pollution standard visual anomaly image data matrix. Search the search space for image data matching the visual features of the water body. Matching the standard visual anomaly image data of water pollution in the water body During the process, the zebra in the visual analysis of the water body moves and changes position towards the direction of the optimal or attacked water body. The calculation formula for the zebra in the visual analysis of the water body moving and changing position towards the direction of the optimal or attacked water body is as follows: ,in This indicates the first of the other predator defense phases. Visual analysis of individual zebras in water bodies, in dimension [missing information]. The water pollution standard visual anomaly image data matrix Moving and changing position within the search space; This indicates that the zebra was attacked during the defense phase of other predators, or that the optimal water visual analysis showed the zebra individual in dimension [missing information]. The water pollution standard visual anomaly image data matrix The position in the search space; Step 2134: Update the optimal solution in the water pollution standard visual anomaly image data matrix. The search space will be used to analyze the visual anomalies of water bodies in zebra individuals and search for the water pollution standard visual anomaly image data. Location and water body visual analysis pioneer Zebra Search retrieved the standard visual anomaly image data of water pollution in the aforementioned water bodies. The location is in the water body water pollution standard visual anomaly image data matrix The fitness values ​​in the search space are compared numerically, and the water pollution standard visual anomaly image data with the highest fitness value is selected. ; Step 2135: When the maximum number of iterations is satisfied... At that time, output the visual feature image data of the water body. The most matching visual anomaly image data of the water body pollution standard. ; Step 2136: Based on the aforementioned visual feature image data of the water body Compared with the standard visual anomaly image data of water pollution in the aforementioned water body The water body visual image feature matching results generate visual anomaly analysis information for the target water body; when and Successful matching of visual features of water bodies indicates that the target water body region has appeared. If a certain type of visual anomaly is identified in the water body, the visual anomaly analysis information for the target water body will be output as "anomaly," and the first... Textual information on visual anomalies related to water pollution in various water bodies; when and If no matching of visual features of the water body is found, it indicates that no visual anomalies of water pollution have appeared in the target water body area, and the output of the visual anomaly analysis information of the target water body is normal.

[0010] Preferably, the steps for collecting water odor information sets and water pollution standard odor anomaly information matrices to identify odor anomalies in the target water body area, and obtaining the target water body odor anomaly identification information, are as follows: During water pollution monitoring operations in target water bodies, drones equipped with odor detectors are used to detect water odor information in the target water area online, and a water odor information set is constructed. ,in Indicates the number of collections The odor information of the water body is collected; the odor detector includes a hydrogen sulfide detection sensor and an ammonia detection sensor. Establish a matrix of abnormal odor information for water pollution standards ,in Indicates the first The standard odor abnormality information for water pollution corresponds to various types of abnormal odors in water bodies; the abnormal odor types include rotten egg smell and pungent urine smell; the standard odor abnormality information for water pollution represents the standard water body odor description text information set for different types of abnormal odors in water pollution. The BERT language model algorithm is used to collect the water odor information set. The water odor information described in The water pollution standard odor anomaly information matrix The abnormal odor information of water pollution standards mentioned in the text Perform text information matching of water body odor, and generate abnormal odor identification information of the target water body based on the text information matching results; when and Successful matching of water odor text information indicates that the target water area has the [number]th [item / item]. If a water body is identified as having an abnormal odor due to water pollution, the corresponding abnormal odor identification information for that water body will be output as "abnormal," and the first... Text information on abnormal odor types of water pollution in various water bodies; when and If no matching of the water body odor text information is found, it means that no abnormal water pollution odor type has appeared in the target water body area, and the output of the target water body odor abnormality identification information is normal.

[0011] Preferably, the water pollution status of the target water body is comprehensively assessed based on the visual and odor detection results of the water body area to obtain water pollution status assessment information. When the status is normal, the current water pollution monitoring operation for the target water body is terminated. When the status is abnormal, the operation steps for the water pollution early warning operation for the target water body are as follows: Based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, a comprehensive judgment and processing of the water pollution status of the target water body area is performed to obtain the water pollution status judgment information of the target water body, which includes normal and abnormal. When the water pollution status judgment information of the target water body is normal, the current water pollution monitoring operation of the target water body ends. When the water pollution status judgment information of the target water body is abnormal, the target water body water pollution early warning operation is performed based on the visual anomaly analysis information and the odor anomaly identification information of the target water body.

[0012] Preferably, based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, a comprehensive judgment of the water pollution status of the target water body area is performed to obtain water pollution status judgment information of the target water body, which includes normal and abnormal. When the water pollution status judgment information of the target water body is normal, the operation steps to end the current water pollution monitoring operation of the target water body are as follows: Obtain visual anomaly analysis information and odor anomaly identification information of the target water body; Based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, a comprehensive judgment and processing of the water pollution status of the target water body area is performed to obtain the water pollution status judgment information of the target water body. When the visual anomaly analysis information of the target water body is abnormal or the odor anomaly identification information of the target water body is abnormal, it indicates that water pollution has occurred in the target water body area, causing the water body to deteriorate, and the water pollution status judgment information of the target water body is output as abnormal. When the visual anomaly analysis information of the target water body is normal and the odor anomaly identification information of the target water body is normal, it indicates that no water pollution has occurred in the target water body area. The water pollution status judgment information of the target water body is output as normal, and the current water pollution monitoring operation of the target water body is directly ended.

[0013] Preferably, when the target water body pollution status judgment information is abnormal, the operation steps for performing the target water body water pollution early warning operation based on the target water body visual anomaly analysis information and the target water body odor anomaly identification information are as follows: When the water pollution status judgment information of the target water body is abnormal, the visual anomaly analysis information and the odor anomaly identification information of the target water body are pushed to the water pollution industrial internet platform through the mobile communication network, and the target water body water pollution monitoring information is displayed and output in conjunction with the display screen to perform the target water body water pollution early warning operation.

[0014] (III) Beneficial Effects This invention provides an intelligent early warning and collaborative management system for water pollution in the industrial internet. It has the following beneficial effects: First, the target water body area feature information is accurately collected through the data input dialog box of the water pollution industrial internet platform, providing reliable data support for remote monitoring of water pollution; based on the target water body area feature information and in conjunction with the GIS system, the geographical coordinates of the target water body area are efficiently and reliably located, realizing precise location of remote water pollution monitoring; based on the target water body area location coordinate data and in conjunction with UAVs carrying water pollution detection equipment, the target water body pollution monitoring operation is autonomously, remotely and safely executed, realizing efficient and safe remote operation control of water pollution based on industrial internet control, improving the flexibility and efficiency of water pollution early warning and monitoring.

[0015] Second, by using drones equipped with cloud lenses to efficiently collect visual feature image data of water bodies, it is possible to scientifically capture abnormal features of water bodies in different regions; based on the visual feature image data of water bodies, combined with artificial intelligence algorithms and standard stored visual anomaly image data of water pollution standards, intelligent analysis of the visual anomaly status of target water bodies is performed, realizing a global scientific analysis of water pollution status based on the visual level of water bodies; by using drones equipped with odor detectors to dynamically collect odor information of water bodies, it is possible to dynamically and accurately capture abnormal odor features of water bodies in different regions; based on the odor information of water bodies, combined with intelligent search algorithms and water pollution standard odor anomaly information based on big data storage, it is possible to efficiently identify the odor anomaly status of target water bodies, realizing efficient identification of water pollution status based on odor detection, and improving the applicability and convenience of water pollution early warning and supervision.

[0016] Third, based on visual anomaly analysis information and odor anomaly identification information of target water bodies, a comprehensive and reliable judgment of the water pollution status of target water bodies is made, realizing remote, efficient, and intelligent monitoring of water pollution status based on vision and odor, and improving the quality and flexibility of water pollution early warning and monitoring; based on visual anomaly analysis information and odor anomaly identification information of target water bodies, combined with the water pollution industrial internet platform and display screen visualization, the target water pollution early warning operation is executed in a timely manner, improving the intuitiveness and speed of water pollution early warning and monitoring, and enhancing the quality of water pollution early warning and monitoring. Attached Figure Description

[0017] Figure 1 A schematic diagram of the modules of the intelligent early warning and collaborative management system for water pollution in the industrial internet provided by the present invention; Figure 2 A flowchart of the operation method of the intelligent early warning and collaborative management system for water pollution in the industrial Internet provided by the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] An example of this intelligent early warning and collaborative management system for water pollution targeting the Industrial Internet is as follows: Example

[0020] Please see Figures 1-2 A smart early warning and collaborative management system for water pollution targeting the industrial internet, comprising the following components: The water pollution monitoring and control module is used to collect feature information of the target water body area, perform geographic location coordinate positioning processing of the target water body area based on the feature information of the water body area, obtain the location coordinate data of the target water body area, and execute water pollution monitoring operations of the target water body. The water pollution visual odor detection module is used to collect visual feature image data of target water bodies, analyze and process visual anomalies in the target water body area to obtain visual anomaly analysis information; collect odor feature information of target water bodies; and identify and process odor anomalies in the target water body area to obtain odor anomaly identification information. The water pollution assessment and early warning module comprehensively assesses the water pollution status of the target water body based on the visual and odor detection results of the water body area, and obtains the water pollution status assessment information of the target water body; when normal, the current water pollution monitoring operation of the target water body ends; when abnormal, the water pollution early warning operation of the target water body is executed. The water pollution monitoring and control module includes a water body area information collection unit, a water body area location coordinate positioning unit, and a water body water pollution monitoring operation execution unit. The water body area information acquisition unit collects characteristic information of the target water body area through the data input dialog box of the water pollution industrial internet platform; the water body area location coordinate positioning unit performs geographic location coordinate positioning processing of the target water body area based on the characteristic information of the target water body area and in conjunction with the GIS system to obtain the location coordinate data of the target water body area; the water body pollution monitoring operation execution unit performs water pollution monitoring operations of the target water body based on the location coordinate data of the target water body area and in conjunction with the water pollution detection equipment carried by the UAV. The water pollution visual odor detection module includes a water body visual feature image acquisition unit, a water body water pollution standard visual anomaly image information storage unit, a water body visual anomaly analysis unit, a water body odor information acquisition unit, a water body water pollution standard odor anomaly information storage unit, and a water body odor anomaly identification unit. The system comprises the following components: a water body visual feature image acquisition unit, which acquires water body visual feature image data via a drone equipped with a cloud camera; a water body water pollution standard visual anomaly image information storage unit, which stores water body water pollution standard visual anomaly image data; a water body visual anomaly analysis unit, which analyzes and processes the visual anomaly status of the target water body area based on the water body visual feature image data and the water body water pollution standard visual anomaly image data to obtain target water body visual anomaly analysis information; a water body odor information acquisition unit, which acquires water body odor information via a drone equipped with an odor detector; a water body water pollution standard odor anomaly information storage unit, which stores water body water pollution standard odor anomaly information; and a water body odor anomaly identification unit, which identifies and processes the odor anomaly status of the target water body area based on the water body odor information and the water body water pollution standard odor anomaly information to obtain target water body odor anomaly identification information. The water pollution assessment and early warning module includes a comprehensive water pollution assessment unit and a water pollution detection and early warning unit; The comprehensive water pollution judgment unit performs a comprehensive judgment on the water pollution status of the target water body area based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, and obtains the water pollution status judgment information of the target water body; the water pollution detection and early warning unit performs water pollution early warning operations on the target water body based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, and in conjunction with the water pollution industrial internet platform and display screen.

[0021] For further details, please refer to Figures 1-2 The operational steps for collecting feature information of the target water body area, locating the target water body area's geographic coordinates based on this feature information, obtaining the target water body area's location coordinate data, and then performing water pollution monitoring operations on the target water body are as follows: Step 11: Collect the geographical name and geographical number of the target water body area online through the data input dialog box of the water pollution industrial internet platform, and obtain the characteristic information of the target water body area; the water pollution industrial internet platform includes any one of the following: Yanyun Smart Water Industrial Internet Platform, Yukong Water Quality Online Monitoring Cloud Platform, and HanClouds Environmental Monitoring Cloud Platform; Step 12: Based on the feature information of the target water body area, perform geographic location coordinate positioning processing of the target water body area, obtain the target water body area location coordinate dataset, and merge it to execute the target water body water pollution monitoring operation.

[0022] The steps for performing water pollution monitoring operations on the target water body by merging the target water body's location coordinate dataset based on the target water body's feature information and then locating the target water body's location coordinate dataset are as follows: Step 121: Using a GIS system, based on the geographic name and geographic number information corresponding to the feature information of the target water body area, the geographical coordinate information of the target water body area is searched in a targeted manner, and a set of location coordinate data of the target water body area is constructed. ,in This indicates the first search result for the target water body area. Location coordinate data of the target water body area, including the longitude, latitude and altitude of the target water body area; Step 122: Using a drone equipped with water pollution detection equipment, based on the target water body area location coordinate data set. Location coordinates of the target water body area To conduct remote water pollution detection and implement water pollution monitoring operations in the target water body area, the water pollution detection equipment includes cloud cameras and odor detectors.

[0023] The water pollution industrial internet platform accurately collects the characteristic information of the target water body area through the data input dialog box, providing reliable data support for remote monitoring of water pollution; based on the characteristic information of the target water body area and in conjunction with the GIS system, the geographical coordinates of the target water body area are efficiently and reliably located, realizing precise location of remote water pollution monitoring; based on the location coordinate data of the target water body area and in conjunction with the drone carrying water pollution detection equipment, the target water body pollution monitoring operation is autonomously, remotely and safely executed, realizing efficient and safe remote operation control of water pollution based on industrial internet control, improving the flexibility and efficiency of water pollution early warning and monitoring.

[0024] For further details, please refer to Figures 1-2 The steps for collecting visual feature image data of the target water body, analyzing and processing the visual anomalies in the target water body area to obtain visual anomaly analysis information, collecting odor feature information of the target water body, and identifying and processing the odor anomalies in the target water body area to obtain odor anomaly identification information are as follows: Step 21: Collect a set of visual feature images of water bodies and a matrix of standard visual anomaly images of water pollution to analyze and process the visual anomaly status of the target water body area, and obtain visual anomaly analysis information of the target water body. Step 22: Collect the water body odor information set and the water body water pollution standard odor anomaly information matrix to identify the odor anomaly status of the target water body area, and obtain the target water body odor anomaly identification information.

[0025] The steps for collecting a dataset of visual feature images of water bodies and a matrix of standard visual anomaly images of water pollution to analyze and process the visual anomaly status of the target water body area, and obtaining visual anomaly analysis information of the target water body, are as follows: Step 211: During the water pollution monitoring operation of the target water body, use a drone equipped with a cloud camera to capture online images of the water body's appearance and construct a set of visual feature images of the water body. ,in Indicates the number of collections Visual feature image data of water bodies; Step 212: Establish a standard visual anomaly image data matrix for water pollution. ,in Indicates the first The standard visual anomaly image data of water pollution corresponding to the visual anomaly types of water pollution; among which the visual anomaly types of water pollution include black water, red water, green water, brown water, turbid water, and floating dead aquatic animals or animals; the standard visual anomaly image data of water pollution represents the standard water body appearance image data set for different water pollution visual anomaly types. Step 213: Collect visual feature image data of water bodies Visual feature image data of water bodies Water pollution standard visual anomaly image data matrix Visual anomaly image data of water pollution standards in medium-sized water bodies Perform visual image feature matching of water bodies, and generate visual anomaly analysis information for the target water body based on the matching results. The specific steps for generating this visual anomaly analysis information are as follows: Step 2131, Initialization Phase: Using the standard visual anomaly image data matrix for water pollution... Within the search space, a set of initial positions of individual zebras for water body visual analysis are randomly generated, and the positions of these zebras within the water body visual analysis zebra individuals are calculated in the water pollution standard visual anomaly image data matrix. The fitness values ​​in the search space are updated and the maximum number of iterations is updated. The formula for calculating the initial position of an individual zebra in the water body visual analysis is as follows: ,in Indicates the first Visual analysis of individual zebras in water bodies, in dimension [missing information]. Water pollution standard visual anomaly image data matrix The initial position in the search space. and These represent the data matrices of visually abnormal images of zebras in water bodies, based on water pollution standards. The upper and lower limits of the search range within the search space. Indicates the value The random number; where the fitness value is calculated using the following formula: ,in Indicates spatial location as Visual analysis of individual zebras in water bodies In the standard visual anomaly image data matrix of water pollution in water bodies The fitness value in the search space. The objective function represents the fitness value; Step 2132, Foraging Behavior Stage, in the water pollution standard visual anomaly image data matrix The individual zebra with the highest fitness value in the zebra population for water body visual analysis within the search space is considered the pioneer zebra for water body visual analysis; the pioneer zebra guides other members of the water body visual analysis zebra population in the water pollution standard visual anomaly image data matrix. The search space is used to move and update to a better position, and the position update formula is as follows: ,in Indicates the first Visual analysis of individual zebras in water bodies, in dimension [missing information]. Water pollution standard visual anomaly image data matrix The update position in the search space; This indicates that the pioneering zebra individuals in water body visual analysis are in dimension [missing information]. Water pollution standard visual anomaly image data matrix The update position in the search space; This represents the control factor controlling individual changes in the zebra population under visual analysis of water bodies, and takes the value of 1 or 2. Step 2133, Defense Strategy Selection Stage: Visual Analysis of Zebras in Water Pollution Standard Visual Anomaly Image Data Matrix The type of defense strategy is determined based on the type of predator detected in the search space; the types of defense strategies include defense against lion attacks and defense against other predators. Step 21331, Lion Attack and Defense Phase: When visual analysis of individual zebras in the water body shows abnormal visual image data matrix according to water pollution standards... When a lion is detected as the predator type in the search space, visual analysis of zebra individuals in the water body shows visual anomalies in the water pollution standard visual image data matrix. Search the search space for image data with visual features of water bodies Matching water pollution standard visual anomaly image data During the process, the location is randomly moved and changed. The formula for calculating the random movement and change of location is as follows: ,in This indicates the first phase of the lion's attack and defense phase. Visual analysis of individual zebras in water bodies, in dimension [missing information]. Water pollution standard visual anomaly image data matrix Random movement and relocation within the search space; Step 21332, Other Predator Defense Phase: When visual analysis of zebra individuals in the water body shows abnormal visual image data matrix according to water pollution standards. When other predators are detected in the search space, the visual analysis of zebra individuals in the water body shows visual anomalies in the water pollution standard visual image data matrix. Search the search space for image data with visual features of water bodies Matching water pollution standard visual anomaly image data During the process, the zebra in the visual analysis of the water body moves and changes position towards the direction of the optimal or attacked water body. The calculation formula for the zebra in the visual analysis of the water body moving and changing position towards the direction of the optimal or attacked water body is as follows: ,in This indicates the first of the other predator defense phases. Visual analysis of individual zebras in water bodies, in dimension [missing information]. Water pollution standard visual anomaly image data matrix Moving and changing position within the search space; This indicates that the zebra was attacked during the defense phase of other predators, or that the optimal water visual analysis showed the zebra individual in dimension [missing information]. Water pollution standard visual anomaly image data matrix The position in the search space; Step 2134: Update the optimal solution in the water pollution standard visual anomaly image data matrix. The search space will include visual analysis of zebra individuals and newly searched water pollution standard visual anomaly image data. Location and visual analysis of water bodies: Zebra Search's standard visual anomaly image data of water pollution. Location in water body water pollution standard visual anomaly image data matrix The fitness values ​​in the search space are compared numerically to select the water pollution standard visual anomaly image data with the highest fitness value. ; Step 2135: When the maximum number of iterations is satisfied... At the same time, output image data of visual features of water bodies. The most matching visual anomaly image data for water pollution standards ; Step 2136: Based on the visual feature image data of the water body Visual anomaly image data related to water pollution standards The water body visual image feature matching results generate visual anomaly analysis information for the target water body; when and Successful matching of visual features of water bodies indicates that the target water body region has appeared. If a certain type of visual anomaly is identified in the water body, the output will be "abnormal" for the target water body's visual anomaly analysis information, and the output will also be "abnormal". Textual information on visual anomalies related to water pollution in various water bodies; when and If no matching of visual features of the water body is found, it means that no visual anomalies of water pollution have appeared in the target water body area, and the output visual anomaly analysis information of the target water body is normal.

[0026] The steps for collecting water odor information sets and using a water pollution standard odor anomaly information matrix to identify odor anomalies in target water bodies are as follows: Step 221: During the water pollution monitoring operation of the target water body, an odor detector equipped with a drone is used to detect the water odor information of the target water body area online, and a water odor information set is constructed. ,in Indicates the number of collections The system provides water odor information; the odor detector includes a hydrogen sulfide detection sensor and an ammonia detection sensor. Step 222: Establish an information matrix of abnormal odors according to water pollution standards. ,in Indicates the first This document provides standard odor abnormality information for water pollution corresponding to various types of abnormal water pollution odors. The types of abnormal water pollution odors include rotten egg odor and pungent urine odor. The standard odor abnormality information represents the textual description of the standard water body odor for each type of abnormal water pollution odor. Step 223: Use the BERT language model algorithm to collect water odor information. Odor information of medium-sized water bodies Odor Anomaly Information Matrix of Water Pollution Standards Odor abnormality information of water pollution standards in medium-sized water bodies Perform text information matching of water body odor, and generate abnormal odor identification information of the target water body based on the text information matching results; when and Successful matching of water odor text information indicates that the target water area has the [number]th [item / item]. If the type of abnormal odor in the water body is identified, the output will show the abnormal odor identification information of the target water body as abnormal, and the output will also show the first abnormal odor identification information. Text information on abnormal odor types of water pollution in various water bodies; when and If no matching of the water body odor text information is found, it means that no abnormal water pollution odor type has appeared in the target water body area, and the output of the target water body odor abnormality identification information is normal.

[0027] By using drones equipped with cloud lenses to efficiently collect visual feature image data of water bodies, abnormal features of water bodies in various regions can be scientifically captured. Based on the visual feature image data of water bodies, combined with artificial intelligence algorithms and standard stored visual anomaly image data of water pollution standards, intelligent analysis of the visual anomaly status of target water bodies is performed, enabling global scientific analysis of water pollution status from a visual perspective. By using drones equipped with odor detectors to dynamically collect odor information of water bodies, abnormal odor features of water bodies in various regions can be dynamically and accurately captured. Based on the odor information of water bodies, combined with intelligent search algorithms and water pollution standard odor anomaly information based on big data storage, abnormal odor status of target water bodies can be efficiently identified, enabling efficient identification of water pollution status based on odor detection, and improving the applicability and convenience of water pollution early warning and supervision.

[0028] For further details, please refer to Figures 1-2 Based on the visual and odor detection results of the water body area, a comprehensive judgment of the water pollution status of the target water body area is made to obtain the water pollution status judgment information of the target water body. When the status is normal, the current water pollution monitoring operation of the target water body ends; when the status is abnormal, the operation steps for the water pollution early warning operation of the target water body are as follows: Step 31: Based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, perform a comprehensive judgment and processing of the water pollution status of the target water body area to obtain the water pollution status judgment information of the target water body, which includes normal and abnormal; when the water pollution status judgment information of the target water body is normal, the current water pollution monitoring operation of the target water body ends. Step 32: When the water pollution status judgment information of the target water body is abnormal, execute the water pollution early warning operation based on the visual anomaly analysis information and the odor anomaly identification information of the target water body.

[0029] Based on visual anomaly analysis information and odor anomaly identification information of the target water body, a comprehensive judgment of the water pollution status of the target water body area is performed to obtain water pollution status judgment information, which includes normal and abnormal conditions. When the water pollution status judgment information of the target water body is normal, the operation steps for ending the water pollution monitoring operation of the target water body are as follows: Step 311: Obtain visual anomaly analysis information and odor anomaly identification information of the target water body; Step 312: Based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, perform comprehensive judgment processing on the water pollution status of the target water body area to obtain the water pollution status judgment information of the target water body; When the visual anomaly analysis information of the target water body is abnormal or the odor anomaly identification information of the target water body is abnormal, it indicates that water pollution has occurred in the target water body area, causing the water body to deteriorate, and the water pollution status judgment information of the target water body is output as abnormal. When the visual anomaly analysis information of the target water body is normal and the odor anomaly identification information of the target water body is normal, it indicates that no water pollution has occurred in the target water body area. The output water pollution status judgment information of the target water body is normal, and the current water pollution monitoring operation of the target water body is directly ended.

[0030] When the water pollution status assessment information of the target water body is abnormal, the operational steps for performing the target water body water pollution early warning operation based on the target water body visual anomaly analysis information and the target water body odor anomaly identification information are as follows: Step 321: When the water pollution status judgment information of the target water body is abnormal, the visual anomaly analysis information and the odor anomaly identification information of the target water body are pushed to the water pollution industrial internet platform through the mobile communication network, and the target water body water pollution monitoring information is displayed and output on the display screen to perform the target water body water pollution early warning operation.

[0031] Based on visual anomaly analysis information and odor anomaly identification information of target water bodies, a comprehensive and reliable judgment of the water pollution status of target water body areas is made, realizing remote, efficient, and intelligent monitoring of water pollution status based on vision and odor, and improving the quality and flexibility of water pollution early warning and monitoring; based on visual anomaly analysis information and odor anomaly identification information of target water bodies, combined with the water pollution industrial internet platform and display screen visualization, the target water pollution early warning operation is executed in a timely manner, improving the intuitiveness and speed of water pollution early warning and monitoring, and enhancing the quality of water pollution early warning and monitoring.

[0032] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart early warning and collaborative control system for water pollution in the industrial internet, characterized in that: The system includes the following: The water pollution monitoring and control module is used to collect feature information of the target water body area, perform geographic location coordinate positioning processing of the target water body area based on the feature information of the water body area, obtain the location coordinate data of the target water body area, and perform water pollution monitoring operations of the target water body. The water pollution visual odor detection module is used to collect visual feature image data of the target water body, analyze and process the visual anomaly state of the target water body area, and obtain visual anomaly analysis information of the target water body. Collect odor characteristic information of the target water body; Odor anomaly identification processing is performed on the target water body area to obtain odor anomaly identification information for the target water body; The water pollution assessment and early warning module performs a comprehensive assessment of the water pollution status of the target water body based on the visual and odor detection results of the water body area, and obtains water pollution status assessment information of the target water body. When the situation is normal, the current water pollution monitoring operation for the target water body will be terminated; when the situation is abnormal, the water pollution early warning operation for the target water body will be executed.

2. The intelligent early warning and collaborative control system for water pollution oriented towards the industrial internet as described in claim 1, characterized in that: The steps for collecting feature information of the target water body area, locating the target water body area's geographic coordinates based on this feature information, obtaining the target water body area's location coordinate data, and then executing water pollution monitoring operations are as follows: The geographical name and geographical number of the target water body area are collected online through the data input dialog box of the water pollution industrial internet platform, and the characteristic information of the target water body area is obtained. Based on the feature information of the target water body area, the geographic location coordinates of the target water body area are processed to obtain the target water body area location coordinate dataset, which is then merged and used for water pollution monitoring operations of the target water body.

3. The intelligent early warning and collaborative control system for water pollution oriented towards the industrial internet as described in claim 2, characterized in that: The steps for performing target water body pollution monitoring operations based on the target water body region's feature information, by locating the target water body region's geographic coordinates, and merging the target water body region's location coordinate dataset, are as follows: Using a GIS system, based on the geographic name and geographic number information corresponding to the feature information of the target water body area, the geographical coordinate information of the target water body area is searched in a targeted manner, and a set of location coordinate data of the target water body area is constructed. The include ;in This indicates the first search result for the target water body area. Location coordinates of each target water body area; Based on the above, water pollution detection equipment is carried by a drone. The above To conduct remote water pollution detection in the target water body area and carry out water pollution supervision operations in the target water body.

4. The intelligent early warning and collaborative control system for water pollution oriented towards the industrial internet as described in claim 3, characterized in that: The steps for collecting visual feature image data of the target water body, analyzing and processing the visual anomalies in the target water body area to obtain visual anomaly analysis information, collecting odor feature information of the target water body, and identifying and processing the odor anomalies in the target water body area to obtain odor anomaly identification information are as follows: The visual anomaly state analysis of the target water body area is processed by collecting a set of visual feature images of water bodies and a matrix of standard visual anomaly images of water pollution to obtain visual anomaly analysis information of the target water body. The odor information set of collected water bodies and the odor anomaly information matrix of water pollution standards are used to identify the odor anomaly status of the target water body area, and the odor anomaly identification information of the target water body is obtained.

5. The intelligent early warning and collaborative control system for water pollution oriented towards the industrial internet as described in claim 4, characterized in that: The steps for collecting a dataset of visual feature images of water bodies and a matrix of standard visual anomaly images of water pollution to analyze and process the visual anomaly status of the target water body area, and obtaining visual anomaly analysis information of the target water body, are as follows: During water pollution monitoring operations in target water bodies, drones equipped with cloud cameras are used to capture online images of the water body's appearance, and a set of visual feature image data of the water body is constructed. The include ;in Indicates the number of collections Visual feature image data of water bodies; Establish a standard visual anomaly image data matrix for water pollution. The include ;in Indicates the first Standard visual anomaly image data of water pollution corresponding to various types of visual anomalies in water bodies; The The above With the The above The specific steps for generating visual anomaly analysis information of the target water body are as follows: Water body visual image feature matching is performed, and visual anomaly analysis information of the target water body is generated based on the matching results. Step 2131, Initialization Phase, in the... Within the search space, a set of initial positions of individual zebras used for visual analysis of water bodies are randomly generated, and the positions of these zebras are calculated. The fitness values ​​in the search space are updated and the maximum number of iterations is updated. ; Step 2132, Foraging Behavior Stage, in the... The individual zebra with the highest fitness value in the zebra population within the search space for water visual analysis is considered the water visual analysis pioneer zebra; the water visual analysis pioneer zebra guides other water visual analysis zebra members in the search space. Move and update to a better position within the search space; Step 2133, Defense Strategy Selection Phase: The zebra visual analysis of the water body... The type of defense strategy is determined based on the type of predator detected in the search space; the types of defense strategies include defense against lion attacks and defense against other predators. Step 21331, Lion Attack and Defense Phase: When the visual analysis of the zebra individual in the water... When a lion is detected as a predator type in the search space, the zebra individual in the water body is visually analyzed. Search the search space for the match with the The matching During the process, the location is randomly moved and changed; Step 21332, Other Predator Defense Phase, when the water visual analysis of the zebra individual is described in the When a predator of type other is detected in the search space, the zebra individual in the water body is visually analyzed. Search the search space for the match with the The matching During the process, the zebras move and change position towards the optimal or attacked water body through visual analysis of the zebra individual. Step 2134: Update the optimal solution, in the... The search space will be used to visually analyze water bodies and search for new zebra individuals. The location and water body visual analysis pioneer Zebra Search found The location is in The fitness values ​​in the search space are compared numerically, and the one with the largest fitness value is selected. ; Step 2135: When the maximum number of iterations is satisfied... At that time, the output is the same as the stated The most matching ; Step 2136, according to the above With the The water body visual image feature matching results generate visual anomaly analysis information for the target water body; when and If the visual image feature matching of the water body is successful, the visual anomaly analysis information of the target water body is output as abnormal, and the first... Textual information on visual anomalies related to water pollution in various water bodies; when and If no matching of visual features of the water body is found, the output of the visual anomaly analysis information of the target water body is normal.

6. The intelligent early warning and collaborative control system for water pollution oriented towards the industrial internet as described in claim 5, characterized in that: The steps for collecting water odor information sets and using a water pollution standard odor anomaly information matrix to identify odor anomalies in target water bodies are as follows: During water pollution monitoring operations in target water bodies, drones equipped with odor detectors are used to detect water odor information in the target water area online, and a water odor information set is constructed. The include ;in Indicates the number of collections Information on water odor; Establish a matrix of abnormal odor information for water pollution standards The include ;in Indicates the first Information on abnormal odors in water bodies according to water pollution standards; The BERT language model algorithm is used to... The above With the The above Perform text information matching of water body odor, and generate abnormal odor identification information of the target water body based on the text information matching results; when and If the text information matching of the water body odor is successful, the abnormal identification information of the target water body odor will be output as abnormal, and the first... Text information on abnormal odor types of water pollution in various water bodies; when and If no matching is found for the text information of water body odor, the output of the abnormal identification information of the target water body odor is normal.

7. The intelligent early warning and collaborative control system for water pollution oriented towards the industrial internet as described in claim 6, characterized in that: Based on the visual and odor detection results of the water body area, a comprehensive judgment of the water pollution status of the target water body area is performed to obtain the water pollution status judgment information of the target water body. When the status is normal, the current water pollution monitoring operation of the target water body ends; when the status is abnormal, the operation steps for the water pollution early warning operation of the target water body are as follows: Based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, a comprehensive judgment and processing of the water pollution status of the target water body area is performed to obtain the water pollution status judgment information of the target water body, which includes normal and abnormal. When the water pollution status assessment information of the target water body is normal, the current water pollution monitoring operation of the target water body ends. When the water pollution status judgment information of the target water body is abnormal, the target water body water pollution early warning operation is performed based on the visual anomaly analysis information and the odor anomaly identification information of the target water body.

8. The intelligent early warning and collaborative control system for water pollution oriented towards the industrial internet as described in claim 7, characterized in that: Based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, a comprehensive judgment of the water pollution status of the target water body area is performed to obtain water pollution status judgment information for the target water body, which includes normal and abnormal conditions. When the water pollution status judgment information of the target water body is normal, the operation steps to end the current water pollution monitoring operation of the target water body are as follows: Obtain visual anomaly analysis information and odor anomaly identification information of the target water body; Based on the visual anomaly analysis information and the odor anomaly identification information of the target water body, a comprehensive judgment and processing of the water pollution status of the target water body area is performed to obtain the water pollution status judgment information of the target water body. When the visual anomaly analysis information of the target water body is abnormal or the odor anomaly identification information of the target water body is abnormal, the water pollution status judgment information of the target water body is output as abnormal. When the visual anomaly analysis information of the target water body is normal and the odor anomaly identification information of the target water body is normal, the water pollution status judgment information of the target water body is output as normal, and the current water pollution monitoring operation of the target water body is directly ended.

9. The intelligent early warning and collaborative control system for water pollution oriented towards the industrial internet as described in claim 8, characterized in that: When the target water body pollution status assessment information is abnormal, the operation steps for performing the target water body pollution early warning operation based on the target water body visual anomaly analysis information and the target water body odor anomaly identification information are as follows: When the water pollution status judgment information of the target water body is abnormal, the visual anomaly analysis information and the odor anomaly identification information of the target water body are pushed to the water pollution industrial internet platform through the mobile communication network, and the target water body water pollution monitoring information is displayed and output in conjunction with the display screen to perform the target water body water pollution early warning operation.

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