CN112907216A - Big data driven self-regulation urban garbage recycling digital twin system - Google Patents
Big data driven self-regulation urban garbage recycling digital twin system Download PDFInfo
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- CN112907216A CN112907216A CN202110257216.6A CN202110257216A CN112907216A CN 112907216 A CN112907216 A CN 112907216A CN 202110257216 A CN202110257216 A CN 202110257216A CN 112907216 A CN112907216 A CN 112907216A
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- 238000004064 recycling Methods 0.000 title claims description 15
- 238000012545 processing Methods 0.000 claims description 5
- 238000013527 convolutional neural network Methods 0.000 claims description 4
- 230000006698 induction Effects 0.000 claims description 3
- 238000011084 recovery Methods 0.000 abstract description 7
- 238000000034 method Methods 0.000 description 13
- 230000008569 process Effects 0.000 description 10
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- 206010062575 Muscle contracture Diseases 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 5
- 230000001105 regulatory effect Effects 0.000 description 4
- 239000002699 waste material Substances 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
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- 206010039203 Road traffic accident Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- G06N3/045—Combinations of networks
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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Abstract
The invention discloses a big data driven self-regulating urban garbage treatment digital twin system. According to the technical scheme, the garbage amount is received by the garbage can in real time in the society and fed back to the digital twin region of the system, the digital twin region carries out self-analysis and arranges places needing to be recovered in severe emergency, moderate emergency and mild emergency, and a garbage recovery route is arranged and scheduled according to real-time road condition information.
Description
Technical Field
The embodiment of the application relates to the field of urban garbage recovery, in particular to a big-data-driven self-regulation urban garbage recovery digital twin system.
Background
Currently, the traditional processing method and means are gradually replaced by emerging technologies such as internet of things, big data, artificial intelligence and the like, and the digital twin technology serving as a digital mapping system of a real physical environment can serve the physical environment to a great extent, so that the physical environment becomes more intelligent, digital and controllable. The method mainly comprises eight strategy strategies: digital network intelligent manufacturing is carried out; the design capability of the product is improved; the technical innovation system of the manufacturing industry is perfected; reinforcing the manufacturing foundation; the product quality is improved; green manufacturing is carried out; the method comprises the following steps of culturing enterprise groups and dominant industries with global competitiveness: the modern manufacturing service industry is developed. The big data driven self-regulation urban garbage recycling digital twin system can realize industry upgrading of the traditional field of garbage recycling based on big data and digital twin technology, green recycling of garbage recycling is realized, and efficiency recycling is realized.
At present, a digital twin system only displays a three-dimensional model and cannot control a physical space according to a predicted condition.
Disclosure of Invention
The embodiment of the application provides a big-data-driven self-regulation urban garbage recycling digital twin system, which is used for constructing and displaying a digital twin section which receives garbage amount fed back to the system through a social real-time garbage can, automatically analyzing and arranging severe emergency, moderate emergency and mild emergency recycling places according to the digital twin section, and arranging and scheduling garbage recycling routes according to real-time road condition information. The digital twinning system comprises a self-collection module, a self-arrangement module and a self-scheduling module.
The technical scheme is that a big data driven self-regulating urban garbage recycling digital twin system comprises:
the self-collection module comprises an information acquisition system and an information positioning processing system which collect garbage putting information from each community, the garbage putting information is summarized, and the self-arrangement module is convenient to process on the next step.
And the self-arranging module comprises a garbage putting information induction system and a garbage information urgency and urgency arrangement system based on a convolutional neural network.
And the self-scheduling module comprises a road condition information system for collecting real-time road condition information and a scheduling system for scheduling and storing garbage routes.
Drawings
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
FIG. 1 is a flow diagram of a big data driven self-regulated municipal waste recovery digital twin system.
101, self-regulating urban garbage recycling digital twin system; 102 a self-collection module; 103 a self-arranging module; 104 from the scheduling module.
Detailed Description
Embodiments of the present disclosure will be described below with reference to the accompanying drawings, wherein well-known functions or constructions are not described in detail in the following description to avoid obscuring the present disclosure in unnecessary detail.
Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs, and the terms "first," "second," and the like, as used herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Furthermore, the terms "a" and "an" do not limit the quantity, but rather denote the presence of at least one of the referenced item. The terms "or" are inclusive and mean any or all of the listed objects, the present specification uses "including," "comprising," or "having" and variations thereof to mean including the objects listed thereafter and equivalents thereof as well as other objects, the terms "connected" and "coupled" are not limited to physical or mechanical connections or couplings, and can include direct or indirect electrical connections or couplings, and furthermore, terms indicating specific positions such as "top," "bottom," "left," and "right" are described with reference to specific drawings, embodiments disclosed in the present disclosure can be arranged in a manner different from that shown in the drawings, and thus, positional terms used in the present specification should not be limited to the positions described in the specific embodiments.
The digital contracture system described in this specification is a high fidelity digital replica or dynamic model of an asset or process that is used to continuously collect data, enhance insight, and simulate a human being to judge and control a pipeline to help people optimize a process from traffic accidents, digital contractures that use data from networks and databases to represent near real-time status and operating conditions of the asset or process have three advantages, e.g., first, the digital contracture has self-learning capabilities and can be continuously learned from new data to improve the process, and second, the digital contracture can be scalable, thus being able to run millions of contractures. Third, the digital contracture may adapt to other parts or process categories, new scenarios or factors.
The present disclosure may apply digital contractures to process planning management of physical systems. Further, in the digital twinning system and method of the present disclosure, the digital contracture body is used not only during operation of the physical system, but also during commissioning of the physical system. A big data driven self-regulated municipal waste recovery digital twin system of the present disclosure will be described in detail below with reference to the accompanying drawings.
A big data driven self-regulating urban garbage recycling digital twin system.
Fig. 1 is a big data driven self-regulated municipal waste recovery digital twin system, as shown in fig. 1, a big data driven self-regulated municipal waste recovery digital twin system 101 comprises a self-collection module 102, a self-arrangement module 103 and a self-scheduling module 104, the self-collecting module 102 processes the data Dai put in real time from the community garbage according to the putting amount and outputs data Da, the self-arranging module 103 processes the data Da through a network protocol, for example, the TCP/IP protocol, receives the data Da and processes it into data Dbi according to the amount of the put, and then graded into data Db by the amount of the put based on the convolutional neural network, the self-scheduling module 104 passes through the network protocol, such as TCP/IP protocol, receives data Db, visualizes data Db at unity3D, passes through network protocol in combination with local real-time road conditions, and e.g. connecting with a High-Level Data Link Control and informing a recycling worker to recycle the garbage according to a route output by the system.
While the disclosure has been illustrated and described in typical embodiments, it is not intended to be limited to the details shown, since various modifications and substitutions can be made without departing in any way from the spirit of the present disclosure, and further modifications and equivalents of the disclosure disclosed in this specification can be made by those skilled in the art using no more than routine experimentation, and all such modifications and equivalents are considered to be within the spirit and scope of the disclosure as defined in the following claims.
Claims (4)
1. A big data driven self-regulating urban garbage recycling digital twin system comprises: the self-collection module comprises an information acquisition system and an information positioning processing system which collect the garbage throwing information from each community, and the garbage throwing information is summarized so as to be convenient for the self-arrangement module to perform the next processing; the self-arranging module comprises a garbage putting information induction system and a garbage information urgency arrangement system based on a convolutional neural network; and the self-scheduling module comprises a road condition information system for collecting real-time road condition information and a scheduling system for scheduling and storing garbage routes.
2. The self-collection module of claim 1, comprising an information acquisition system and an information positioning processing system for collecting the garbage input information from each community, wherein the garbage input information is summarized to facilitate further processing by the self-ranking module.
3. The self-ranking module of claim 1 comprising a spam induction system and a convolutional neural network-based spam urgency ranking system.
4. The self-scheduling module comprises a road condition information system for collecting real-time road condition information and a scheduling system for scheduling and storing garbage routes.
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| CN202110257216.6A CN112907216A (en) | 2021-03-10 | 2021-03-10 | Big data driven self-regulation urban garbage recycling digital twin system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116108692A (en) * | 2023-03-15 | 2023-05-12 | 北京祝融视觉科技股份有限公司 | Digital twin system for urban infrastructure |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111178662A (en) * | 2019-11-06 | 2020-05-19 | 南京君慕士物联网技术研究院有限公司 | Garbage disposal system based on Internet of things |
| CN111260205A (en) * | 2020-01-13 | 2020-06-09 | 广东生活环境无害化处理中心有限公司 | Transportation planning system for collecting medical waste |
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- 2021-03-10 CN CN202110257216.6A patent/CN112907216A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111178662A (en) * | 2019-11-06 | 2020-05-19 | 南京君慕士物联网技术研究院有限公司 | Garbage disposal system based on Internet of things |
| CN111260205A (en) * | 2020-01-13 | 2020-06-09 | 广东生活环境无害化处理中心有限公司 | Transportation planning system for collecting medical waste |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116108692A (en) * | 2023-03-15 | 2023-05-12 | 北京祝融视觉科技股份有限公司 | Digital twin system for urban infrastructure |
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