CN115372571A - Intelligent water environment monitoring system - Google Patents
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Abstract
The invention discloses an intelligent monitoring system for water environment, which comprises: the cloud intelligent identification rule base stores intelligent identification rules; the edge side intelligent identification rule base receives and stores the intelligent identification rules issued by the cloud intelligent identification rule base; the edge analysis module extracts an intelligent identification rule from an edge side intelligent identification rule base, identifies the water environment video data by using the intelligent identification rule to obtain water body characteristics, outputs analysis results of the water body characteristics and/or various water quality indexes according to the water environment evaluation rule, displays and alarms abnormal results, and sends data which cannot be analyzed and determined to the cloud comprehensive analysis module; and the cloud comprehensive analysis module receives and analyzes the data sent by the edge analysis module, and displays and alarms abnormal results. The invention constructs a video identification model based on machine learning, and forms an omnibearing water environment monitoring system by combining with a detector, thereby realizing the intelligent identification of the water environment.
Description
Technical Field
The invention relates to application of an Internet of things and video analysis technology in the field of intelligent supervision of water quality, in particular to an intelligent sewage operation supervision comprehensive management method based on the intelligent analysis technology of the Internet of things and video identification. Through the technology, a more comprehensive and more real-time water environment intelligent monitoring system is innovated.
Background
The sewage and industrial waste water discharged by urban residents in daily life contain a large amount of organic pollutants, and rivers and reservoirs in most cities have serious organic pollution. This results in a moderate reduction of urban water sources and an increase of treatment costs, which seriously threatens the health of urban residents. The harm caused by water pollution has wide influence range and long duration, and the harm is usually highlighted after a long period of time. In addition, water pollution will exacerbate water resource scarcity and worsen the ecological environment. Therefore, the importance of water environment treatment and water business industry is increasingly prominent.
In general routine inspection of water management, inspection personnel basically conduct inspection of water environment related problems of pipe flow areas in various forms such as regular inspection, routine inspection, special inspection, seasonal inspection and the like according to a set inspection plan. Basically, water quality monitoring is to collect water quality samples periodically and manually to perform chemical examination so as to obtain the current detection result of the current water quality. No matter it is daily to patrol and examine, still water quality censorship, all have and can't guarantee to monitor at every moment in real time, can't discover the water affair problem fast, can't accomplish 7 by 24 hours incessant monitoring water affair situations too.
Disclosure of Invention
Therefore, the invention provides a comprehensive analysis method for water affair supervision. The intelligent water affair monitoring and managing system is mainly based on the internet of things, machine learning and video identification analysis technology, obtains and standardizes problem data such as on-site water quality information, water level conditions and river channel garbage floaters, achieves integration of data acquisition, data transmission and data processing, and further achieves overall monitoring and management of intelligent water affairs. The invention adopts the following specific scheme:
an intelligent monitoring system for water environment, comprising:
the camera A is arranged at each first detection point of the water area and is used for capturing water environment video data;
the water quality detection detector B is arranged at each second detection point of the water area and is used for detecting various water quality indexes;
the cloud intelligent identification rule base E2 stores intelligent identification rules, and the intelligent identification rules comprise water environment evaluation rules and various video identification models formed through machine learning;
the edge side intelligent recognition rule base E1 receives and stores the intelligent recognition rules issued by the cloud side intelligent recognition rule base E2;
the edge analysis module D extracts an intelligent identification rule from the edge side intelligent identification rule library E1, identifies the water environment video data by using the video identification model to obtain water body characteristics, outputs an analysis result of the water body characteristics and/or various water quality indexes according to the water environment evaluation rule, transmits an abnormal result to the edge end alarm display module C1 for display and alarm, and sends data which cannot be analyzed and determined to the cloud comprehensive analysis module F;
and the cloud comprehensive analysis module F receives and analyzes the data sent by the edge analysis module D, and transmits the abnormal result to the cloud alarm display module C2 for display and alarm.
Optionally, the first detection point is not identical to the second detection point.
Optionally, the edge alarm display module C1 and the cloud alarm display module C2 both perform alarm prompting by sound and/or light.
Optionally, the cloud alarm display module C2 further receives and displays the alarm sent by the edge alarm display module C1.
Optionally, the water environment evaluation rules include a first water environment evaluation rule and a second water environment evaluation rule, the first water environment evaluation rule determines a first water environment evaluation level according to various water quality evaluation criteria, and the second water environment evaluation rule performs graded evaluation on the water characteristics to determine a second water environment evaluation level.
Optionally, a video recognition model recognizes the water quality through the river water surface color of the region, and the training process is to extract the water surface colors of different time periods and different water quality levels of the region to perform machine learning, so as to obtain second water environment evaluation levels of different water qualities of different levels of the region in different time periods.
Optionally, the various water quality detecting detectors B are distributed at different regional locations for detecting various factors affecting water quality, including a natural water source detector, a town water system detector, a water mouth detector, and a pollution source detector.
Optionally, the camera a supports infrared, panoramic 360-degree rotation, and synchronous audio, and the edge analysis module D is integrated inside the camera device, or is arranged independently.
Optionally, for data of the water environment evaluation rule without matching, the edge analysis module sends an alarm signal to the edge alarm display module C1, and the cloud comprehensive analysis module F sends an alarm signal to the cloud alarm display module C2.
Optionally, the system further comprises an edge data storage module G1 for storing water environment video data, various water quality index data collected by the detector, and analysis result data of the edge analysis module D;
and the cloud data storage module G2 is used for storing the collected data transmitted to the cloud, the analysis result and the alarm record.
The invention discloses a video identification model constructed based on machine learning, which is combined with a detector to form an all-dimensional water environment monitoring system. And, the terminal edge analysis module effectively carries partial data analysis and data storage functions. By the aid of the mode, water environment intelligent identification and real-time monitoring are really realized, and a more accurate water environment monitoring mode is realized according to local conditions.
Drawings
Fig. 1 is an overall frame diagram of an intelligent monitoring system for water environment according to an embodiment of the invention;
fig. 2 is a flow chart illustrating the operation of the intelligent monitoring system for water environment according to the embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the water environment intelligent monitoring system of this embodiment includes a camera a, a water quality detection detector B, an edge analysis module D, an edge side intelligent recognition rule base E1, a cloud comprehensive analysis module F, a cloud intelligent recognition rule base E2, an edge data storage module G1, a cloud data storage module G2, and a data transmission module H. The method is used for monitoring the water environment condition of the water area, wherein the water environment condition comprises a water quality condition, a water level condition, a water surface condition and the like.
The cameras A can be multiple and are arranged at first detection points in a water area, and the first detection points are distributed at positions of a river channel, a reservoir, a sewage drainage port, a water level marking line and the like. The camera a is used for capturing real-time scenes, for example, collecting relevant video data such as water color, water surface floating objects, water level and the like. The camera includes but is not limited to supporting infrared, 360-degree panoramic rotation up, down, left and right, supporting synchronous audio and the like.
The water quality detection detectors B are distributed at second detection points of the water area, and the second detection points can be completely the same as the first detection points, can also be completely different from the first detection points, or are not completely the same. The water quality detection detector B is used for detecting various factors influencing water quality, and comprises but is not limited to a natural water source detector (arranged in a river channel), a town water supply system detector (arranged in a town water supply system), a water consumption detector (arranged at a water consumption terminal), a pollution source detector (arranged at a possible pollution source), and the acquired data is used for analyzing a terminal edge analysis and processing subsystem. The water quality detector may be an integrated detector including, for example, a temperature sensor, a turbidity sensor, a conductivity sensor, a microorganism sensor, a COD sensor, an NH3-N sensor, a dissolved oxygen sensor, etc., or may include such sensors.
The edge analysis module D extracts intelligent identification rules from the edge side intelligent identification rule base E1, the intelligent identification rules comprise various video identification models formed through machine learning and water environment evaluation rules, and after some data are analyzed through the edge analysis module, only abnormal analysis results and corresponding monitoring data need to be transmitted to a cloud platform, and qualified analysis results and monitoring data do not need to be transmitted to a cloud comprehensive analysis module for analysis. For example, a video recognition model for judging the water quality category based on the water surface color of a river channel in a certain region is provided, the water surface color data collected by the camera A in the region is analyzed in the edge analysis module D, the data qualified in analysis is not transmitted to the cloud comprehensive analysis module, and the data and the result unqualified in analysis result are transmitted to the cloud comprehensive analysis module.
The video identification model and the related evaluation rule in the edge side intelligent identification rule base E1 are issued by the cloud comprehensive analysis module, so that the edge side intelligent identification rule base is a subset of the cloud comprehensive intelligent identification rule base. The water environment evaluation rules comprise a first water environment evaluation rule and a second water environment evaluation rule, the first water environment evaluation rule determines a first water environment evaluation level according to various water quality evaluation standards, and the second water environment evaluation rule performs graded evaluation on the water characteristics extracted by the video to determine a second water environment evaluation level. The edge analysis module D may perform evaluation by combining the water characteristics extracted by the video with the second water environment evaluation rule alone to output the second water environment level, may perform evaluation by combining the detection data of the detector with the first water environment evaluation rule alone to output the first water environment level, may perform comprehensive consideration on the evaluation results of the first water environment evaluation rule and the second water environment evaluation rule, and outputs a comprehensive water environment level, which may be, for example, weighted sum output. And judging the abnormal result refers to comparing the water environment evaluation level with a set threshold level, and if the water environment evaluation level exceeds the threshold level, judging the abnormal result.
The video recognition model can be trained in a machine learning mode, the video recognition model is trained through various data including training data of various water body colors related to water environments, or training data of sizes, numbers, density degrees and the like of floating objects on the water bodies related to the water environments, and the like, the video recognition model can be recognized by extracting images from videos in a form of extracting image frames, and a second water environment grade is determined from a second water environment evaluation rule according to a recognition result.
The video recognition models may be a plurality of models, such as a floater recognition model, a water quality recognition model (through water quality color analysis), and the like, and may be set corresponding to each video recognition modelSetting different second water environment evaluation rules, for example, corresponding to the floater identification model, the second water environment evaluation rules can be set according to the size of the floater, and the coverage area of the floater is less than 10cm 2 The number of the floating objects is less than 3, the floating objects correspond to one water environment grade, and the coverage area of the floating objects is more than 10cm 2 Less than 30cm 2 The number of the floating objects is less than 3, the floating objects correspond to one water environment grade, and the coverage area of the floating objects is more than 30cm 2 Less than 1m 2 And more than 3 correspond to one water environment level. It can be seen that as the coverage area of the floating objects is increased and the number of the floating objects is increased, the corresponding grade of the second water environment is lower and lower. And the second water environment evaluation rule corresponds to the video identification model, so that the second water environment grade can be output according to the rule after the result is identified by the video identification model.
For example, corresponding to the water quality identification model, a second water environment evaluation rule can be set according to the different colors of water quality, the water quality is identified through the water surface color of a river in a certain region, and then the training of the water quality identification model is to extract different time periods of the region, and the water surface colors of different water quality levels are subjected to machine learning to obtain the rules of different water qualities of the region in different time periods. The different time periods may be, for example, four seasons of spring, summer, fall and winter. And comparing the water quality identification model with a second water environment evaluation rule according to the identification result so as to determine a corresponding second water environment grade (of course, the water quality judgment at the position is not accurate enough through the water color, and the comprehensive water quality grade can be determined by combining with the data of the detector). And identifying and coding each first water environment evaluation rule and each second water environment evaluation rule, wherein each specific rule corresponds to one code. And preferably, each rule identifier is also given to belong to edge side intelligent identification or cloud intelligent identification.
The detectors at different positions can adopt the same rule to judge the water environment level, for example, the data of the COD sensors at different positions adopt the same first water environment evaluation rule to judge the first water environment level.
And analyzing the video image collected by the camera A in real time. Because the video image is usually large, it takes a lot of time and storage space to transmit the video image to the cloud comprehensive analysis module F, and therefore the edge analysis module D recognizes the video image at the edge side as far as possible through the video data acquired in real time, rather than transmitting the video image to the cloud comprehensive analysis module F for recognition. The edge analysis module D can be integrated in the camera device, and a data analysis and acquisition box can be independently designed. The embodiment is not important in the present invention, but the functionality carried by this module.
After receiving the relevant data acquired by the camera or the detector, the edge analysis module D firstly goes to the edge side intelligent identification rule base E1 to inquire whether the local intelligent identification rule can be applied or not, if so, the video identification module and the water environment evaluation rule are called to carry out analysis, and the analyzed normal data are stored in the data storage module according to the set effective time. If the data is abnormal data, the edge alarm display module C1 is called, and related alarm content is synchronously transmitted to the cloud alarm display module C2.
For example, the camera a captures the floating objects on the water surface, the edge analysis module analyzes the image of the video frame by using the video identification module, and judges whether the data is abnormal data according to the water environment evaluation rule, for example, the coverage area of the floating objects on the water surface is larger than 1m 2 If the result is abnormal, the edge alarm display module C1 synchronously sends out an alarm. Meanwhile, the alarm result is transmitted back to the cloud alarm display module C2, after receiving the alarm, the control room personnel check the video to confirm that the large garbage really exists on the water surface, and then inform patrol personnel to clean the garbage on the spot.
The cloud comprehensive analysis module F mainly analyzes data which are transmitted by the camera A and various water quality detectors and are not processed by the edge end analysis module, and calls the cloud side intelligent identification rule E2 module as an identification model and an identification basis. For example, the recognition accuracy of the recognition model which is learned by the machine at present can only reach 80%, at the moment, the recognition is not very accurate only by relying on the recognition model, and if a corresponding water quality detector is arranged at a measured point, the data transmitted back by the detector can be combined for analysis. If the data of the detector cannot be acquired, the data are returned to the cloud comprehensive analysis module, and personnel in a control room manually analyze the video data. For another example, the detector data of the detection points are easily affected by seasons and environments, and no unique fixed monitoring standard exists, so that the cloud comprehensive analysis module can call real-time data of local areas, which affect the detection standard, such as rainfall, temperature, displacement and the like, to perform comprehensive analysis, and accordingly the rules are corrected. The specific content belongs to the water process part, and is not explained in detail herein. And (5) processing by a comprehensive analysis module to obtain the water environment grade.
After receiving the data to be processed, the cloud comprehensive analysis module F calls a cloud side intelligent identification rule E2; and if the matched water environment evaluation rule is not inquired, informing related technicians of manual analysis in an alarm mode. For example, data of the concentration of a certain chemical component of the sewage outlet returned by the detector is received, but no relevant rule of the detector is found in the water environment evaluation rule, and then the cloud alarm display module C2 is called to remind control room personnel in a sound mode, and manual analysis is needed when data which cannot be analyzed is received.
And if the matched water environment evaluation rule is inquired and the data of a certain detector is required to be combined for comprehensive analysis, calling a data storage module to acquire the real-time data of the detector for comprehensive analysis. Certainly, when the water environment evaluation rules are configured, which video identification model needs to correspond to which second water environment evaluation rule, which detector needs to correspond to which first water environment evaluation rule, and which video identification model is matched with which detector to perform comprehensive evaluation can be configured according to needs. Such as a rule relating water quality color to water contaminant composition. The cloud comprehensive analysis module receives data of abnormal water quality and color returned by a certain camera, for example, the returned color is black, calls a video identification model, needs to be comprehensively analyzed by combining data of a certain detector, finds that the content of manganese returned by the data of the detector seriously exceeds the standard, obtains the final comprehensive water environment grade according to the degree of the water quality color and the manganese content exceeding the standard, and displays the final comprehensive water environment grade in an alarm mode.
Cloud side intelligent recognition rule base E2: the part comprises a video identification model and different set water environment evaluation rules. The rule base E2 can be customized and configured according to requirements, and subsequently, with machine learning covering more recognition scenes and changes of water quality standards, the intelligent recognition rule base E2 can be updated in a parallelization mode. And after the cloud side intelligent identification rule base E2 is updated, the edge side intelligent identification rule base E1 can be synchronously updated.
And after receiving the data to be processed, the cloud comprehensive analysis module F calls a cloud side intelligent identification rule base E2, and if a matched water environment evaluation rule is inquired, a related rule and a third-party interface are called to analyze the data. If the data is abnormal data, the cloud alarm display module C2 needs to be called. For example, the cloud comprehensive analysis module F receives water level data of a river, finds a water environment evaluation rule related to water level identification in the rule base E2, identifies that comprehensive analysis needs to be performed by calling a three-party interface (in the current region, in the current weather, in the current rainfall, etc.), and stores the analyzed normal data in the data storage module according to the set effective storage time. If the data is abnormal data, the alarm display module C2 is required to be called.
The data storage module G1 belongs to the side of the cloud end and comprises all collected data, analysis results and alarm records which are transmitted to the cloud end. The data storage module can adopt a centralized conventional database for storage and can also adopt a non-centralized encryption mode based on a block chain for storage.
The data storage module G2 belongs to the terminal side. Some video data in the effective time, the data that the detector gathered supply high in the clouds integrated analysis module to call.
The data transmission module H receives various data transmitted back by the edge analysis module, the camera and the related water quality detector at the terminal side, and sends an intelligent identification rule which is issued from the cloud to the edge analysis module.
The edge end alarm display module C1 is used for carrying out alarm recording and reminding on the measured point with unqualified water environment grade after the detection of the video recognition model and the detector. C1 is deployed at the terminal side. The alert reminder includes but is not limited to sound, light grading, etc. After the edge end analysis module processes, the data is unqualified, for example, the face appears on the river surface through video recognition, abnormal data is returned to the alarm display module, the edge end alarm display module C1 reminds in a sound mode, and the alarm generated by the edge end alarm display module C1 needs to return the result to the cloud alarm display module synchronously.
And the cloud warning display module C2 is used for analyzing and processing the unqualified data by the cloud comprehensive analysis module, and the cloud warning display module C2 is used for carrying out combination prompt in a mode of combining sound with lamplight. And may also receive alarms from the C1 module.
The following describes a working process of the intelligent water environment monitoring system, the cloud side intelligent identification rule base E2 synchronizes some rules to the edge side intelligent identification rule base E1, the edge analysis module D searches for a water environment evaluation rule by removing the edge side intelligent identification rule base E1 after receiving data of a camera and a detector, for example, if the data is video data, a second water environment evaluation rule is checked for evaluation, if the data is detector data, a first water environment evaluation rule is checked for evaluation, normal data is stored in the data storage module G2, and abnormal data is stored in the edge end alarm display module C1.
If the edge side intelligent identification rule base E1 does not have the corresponding water environment evaluation rule, the edge analysis module D sends data to the cloud comprehensive analysis module F, the cloud comprehensive analysis module F goes to the cloud side intelligent identification rule base E2 to check and receive the water environment evaluation rule, and evaluation can be carried out by combining the first evaluation rule and the second evaluation rule.
And if the cloud end side intelligent identification rule base E2 does not have the corresponding water environment evaluation rule, giving an alarm to technical personnel for manual analysis. If the evaluation data is only the detector data, the cloud comprehensive analysis module F can call a third-party interface to obtain relevant evaluation data for evaluation. For example, for water level data sent by the detector, the cloud comprehensive analysis module F may call a third-party interface to obtain the current weather, the current rainfall and other data of the area where the water area is located, and then comprehensively analyze the water level to give water level evaluation.
The present invention is capable of other embodiments, and various changes and modifications can be made by one skilled in the art without departing from the spirit and scope of the invention.
Claims (10)
1. An intelligent monitoring system for water environment, which is characterized by comprising:
the camera A is arranged at each first detection point of the water area and is used for capturing water environment video data;
the water quality detection detector B is arranged at each second detection point of the water area and is used for detecting various water quality indexes;
the cloud intelligent identification rule base E2 stores intelligent identification rules, and the intelligent identification rules comprise water environment evaluation rules and various video identification models formed through machine learning;
the edge side intelligent recognition rule base E1 receives and stores the intelligent recognition rules issued by the cloud side intelligent recognition rule base E2;
the edge analysis module D extracts intelligent identification rules from the edge side intelligent identification rule library E1, identifies the water environment video data by using the video identification model to obtain water body characteristics, outputs analysis results according to the water environment evaluation rules for the water body characteristics and/or various water quality indexes, transmits abnormal results to the edge end alarm display module C1 for display and alarm, and sends data which cannot be analyzed and determined to the cloud comprehensive analysis module F;
and the cloud comprehensive analysis module F receives and analyzes the data sent by the edge analysis module D, and transmits the abnormal result to the cloud warning display module C2 for displaying and warning.
2. The intelligent monitoring system for water environment according to claim 1, wherein the first detection point is not identical to the second detection point.
3. The intelligent water environment monitoring system according to claim 1, wherein the edge alarm display module C1 and the cloud alarm display module C2 are configured to perform alarm prompting by sound and/or light.
4. The intelligent water environment monitoring system according to claim 1, wherein the cloud alarm display module C2 further receives and displays an alarm sent by the edge alarm display module C1.
5. The water environment intelligent monitoring system according to claim 1, wherein the water environment evaluation rules comprise a first water environment evaluation rule and a second water environment evaluation rule, the first water environment evaluation rule determines a first water environment evaluation level according to various water quality evaluation criteria, and the second water environment evaluation rule performs graded evaluation on water body characteristics to determine a second water environment evaluation level.
6. The intelligent water environment monitoring system of claim 1, wherein a video recognition model recognizes water quality by the color of the surface of the river of a region, and the training process is to extract the colors of the water surface of the region at different time periods and different water quality levels for machine learning to obtain second water environment evaluation levels of the region at different time periods and different water quality levels.
7. The intelligent monitoring system for water environment according to claim 1, wherein various water quality detection detectors B are distributed at different regional locations for detecting various factors affecting water quality, including natural water source detectors, town water supply system detectors, water mouth detectors, pollution source detectors.
8. The intelligent monitoring system for water environment according to claim 1, wherein the camera a supports infrared, panoramic 360-degree rotation, and synchronous audio, and the edge analysis module D is integrated inside the camera device or arranged independently.
9. The system for intelligently monitoring the water environment according to claim 1, wherein for the data of the non-matched water environment evaluation rule, the edge analysis module sends an alarm signal to the edge alarm display module C1, and the cloud comprehensive analysis module F sends an alarm signal to the cloud alarm display module C2.
10. The intelligent monitoring system for water environment according to claim 1,
the system also comprises an edge data storage module G1, which stores water environment video data, various water quality index data collected by the detector and analysis result data of an edge analysis module D;
and the cloud data storage module G2 is used for storing the acquired data transmitted to the cloud, the analysis result and the alarm record.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116233370A (en) * | 2023-04-27 | 2023-06-06 | 安徽哈斯特自动化科技有限公司 | Intelligent video monitoring method based on water quality monitoring |
| CN117274827A (en) * | 2023-11-23 | 2023-12-22 | 江苏国态环保集团有限公司 | Intelligent environment-friendly remote real-time monitoring and early warning method and system |
| CN117804575A (en) * | 2024-02-29 | 2024-04-02 | 熊猫智慧水务有限公司 | Intelligent water affair measuring method and system suitable for SIS |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116233370A (en) * | 2023-04-27 | 2023-06-06 | 安徽哈斯特自动化科技有限公司 | Intelligent video monitoring method based on water quality monitoring |
| CN116233370B (en) * | 2023-04-27 | 2023-07-25 | 安徽哈斯特自动化科技有限公司 | Intelligent video monitoring method based on water quality monitoring |
| CN117274827A (en) * | 2023-11-23 | 2023-12-22 | 江苏国态环保集团有限公司 | Intelligent environment-friendly remote real-time monitoring and early warning method and system |
| CN117274827B (en) * | 2023-11-23 | 2024-02-02 | 江苏国态环保集团有限公司 | Intelligent environment-friendly remote real-time monitoring and early warning method and system |
| CN117804575A (en) * | 2024-02-29 | 2024-04-02 | 熊猫智慧水务有限公司 | Intelligent water affair measuring method and system suitable for SIS |
| CN117804575B (en) * | 2024-02-29 | 2024-05-03 | 熊猫智慧水务有限公司 | Intelligent water affair measuring method and system suitable for SIS |
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