| 英文摘要 |
This project uses artificial intelligence image recognition technology to complete smoke opacity identification, and achieves the project results are as follows:
An integration of the intelligent smoke detection system and an overall architecture prototype of the cloud service module has been completed, which includes an intelligent image recognition system. Regarding the opacity recognition of the system, the average opacity recognition rate for black and white smoke has stabilized between 5 % to 7 %, with the maximum error for black smoke being less than 15 %, while the maximum error for white smoke remains above 15 %.
The draft of the intelligent smoke recognition method, which is ”A draft of photographic determination method of opacity of particulate pollutants discharged from chimney“ has been formulated, referencing domestic and foreign methods (such as visual smoke recognition training manuals, method 9, and ASTM 7520-2016), as well as expert suggestions.
In the process of establishing the verification method for the intelligent smoke image recognition module and its software and hardware verification program, we completed the construction of the verification environment, including a high-intensity light source, a set of replica standard grayscale boards, which’s maximum error is less than 3 % for the four opacity levels corresponding to that of BSI 2742M smoke grayscale standard board.
An indoor calibration document for the intelligent image smoke detection and recognition module was completed, including a draft of the indoor calibration procedure, a quality assurance plan with uncertainty assessment, and a recommended equipment list.
Seven rounds of equipment verification for image capture and processing were carried out, and three pieces of equipment passed the verification.
The software verification program for the intelligent smoke recognition system has been established. Field inspections were conducted at the Taoyuan and Tainan smoke detection centers, a library of 50 standard image data of black smoke and white smoke was established with an average opacity error of less than 7 %.
The draft outdoor verification procedure has been completed. A filed test of outdoor verification experiment was conducted with four trials each for black smoke and white smoke. A set of black smoke simulation verification for the smoke detection system was completed, and the verification results exceeded the standards of visual smoke detection.
We have completed three promotion and explanation session with 126 attendees to support the Environmental Protection Administration's promotion of the intelligent image-based smoke recognition system and other related matters. Additionally, we have assisted the Administration in conducting 15 regular project work review meetings.
Complete the benefit of intelligent smoke identification task, benefit of personnel training and analysis of automated operation analysis for economic benefit analysis by this project.
In terms of possible social and economic impacts, the smart image smoke detection system can be used as a factory-side self-managed monitoring system in the future to help achieve the long-term goals of carbon reduction and zero carbon, and reduce the social and economic costs caused by air pollution.
In terms of economic benefit analysis, in terms of benefits of the smart image smoke detection system, the time efficiency is increased by 66 %, the error rate is reduced by 1/3, the drift of the smoke stream is uncertain, and the limitation of insufficient illumination is broken.
In terms of the effectiveness of the image smoke recognition system, the influence of subjective judgments of visual inspection can be reduced.
In terms of the benefit of personnel training, it can reduce the input of manpower, thereby reducing the training cost. The benefits of automated operations, fixed systems can use remote operations, reduce the cost of personnel dispatched tasks, and improve the overall monitoring efficiency.
|