There is no rubbish in the world, only treasures misplaced. This sentence of Italian poet Dante can be understood as garbage classification is to rediscover the resources misplaced, so as to reduce garbage and recycle resources. The implementation of waste classification is not only related to the living environment and the economical utilization of resources, but also one of the important symbols of a civilized society. To this end, all localities have successively issued waste classification management measures, defined the detailed rules for classified delivery, collection, transportation, disposal, supervision and management of waste, and equipped with waste classification and delivery facilities.

However, the introduction of policies and the availability of facilities do not mean the smooth implementation of waste classification. The phenomena of mixed input, mixed collection, mixed loading and mixed transportation of waste still exist to varying degrees. To promote the implementation of waste classification, the key lies in using digital technology to enhance the efficiency of release control, improve the waste tracking ability, realize the recordability during release, the analysis of problems, the recordability during transportation and the management during disposal, and form a closed loop of the whole process of classified release, classified collection, classified transportation and classified treatment.

Tracing of classified delivery violations

In recent years, after extensive publicity and popularization, the concept of waste classification has been deeply rooted in the hearts of the people, and the vast majority of citizens have formed the conscious behavior of waste classification. Unfortunately, there are still a group of people who mix garbage for convenience, throw it away and leave. Although many communities arrange manual supervision of residents’ garbage release, it is difficult to form long-term supervision.

In this regard, digital intelligence source has comprehensively applied AI video recognition algorithm, AR video enhanced fusion, BI data analysis and other core technologies to launch a waste classification visual supervision platform, and applied AI + Ar + bi technology to various scenes of classified delivery, classified collection, classified transportation and classified treatment, enabling fine management of waste classification and creating an intelligent management mode of the whole process.

In the classified release scenario, the digital Zhiyuan waste classification visual supervision platform is based on the camera and perceptron, and the system can upload the whole release process of residents to the supervision background in real time. When residents enter the recognition area, the system can automatically perceive the garbage delivery behavior, and make the garbage delivery more convenient through AI image recognition technology and voice broadcasting. If residents throw garbage in violation of regulations, the system will collect images and image fixed evidence of violators, so as to facilitate the accurate processing of urban management departments.

Classification collection and whole process visualization

Although the garbage classification policy has been implemented, there are still many problems in the classified collection link, among which the most common is mixed collection and transportation. In the long run, both residents and supervisors will neglect the classification standards because of the non-standard collection end. Because no matter how well the front end is divided, it is finally pulled away by one car, and the delivery end is equivalent to doing useless work. Therefore, it is necessary to improve the collection system. Based on this, how to better solve the problem of mixed collection and transportation?

Based on the geographic information system, the digital intelligence source waste classification visual supervision platform carries out real-time visual monitoring of waste classification and collection, supports the integration of video monitoring and monitoring data uploaded by sensors in real time, automatically identifies the overflow status of waste bins, and uploads the information to the command and dispatching platform in real time. The platform notifies nearby waste collection vehicles according to the alarm location of waste bins, and monitors the clearing and transportation records in real time through video, Determine the collection type, times and weight by means of IOT sensing to avoid mixed loading and transportation.

Fine management of transport vehicles

Classified transportation is the key link between recycling and disposal, but there are still some bad phenomena in the process of waste transportation, such as running, dripping and leaking, waste dragging and hanging. Therefore, it is necessary to strengthen the supervision of transportation links, and continuously promote and promote the back-end improvement of waste classification with digital technology, so that people can truly feel the effect of waste classification.

Based on GIS, the digital intelligence source waste classification visual supervision platform supports the visual real-time monitoring of kitchen waste trucks, kitchen waste trucks, closed compression trucks and other waste trucks, and displays the details of license plate number, vehicle type, affiliated company, property right unit, waste source, current load, historical waste collection and transportation tons and waste destination, as well as the real-time line and historical track of vehicles, Realize the fine management of transportation vehicles.

epilogue

THE END

Digital Zhiyuan waste classification visual supervision platform can effectively solve the difficulties and pain points in waste classification management, realize waste classification supervision visualization, risk early warning and problem traceability, and facilitate managers to make efficient and scientific decisions based on data analysis, which will promote citizens and communities to self regulate with higher requirements, do a good job in waste classification, and usher in a new fashion era of low-carbon life.

Reviewed and edited by: Peng Jing

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