# US 2023/0057564 A1 · Methods for managing cleaning routes in smart cities, internet of things systems, and storage mediums

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2023/0057564 A1
- Document ID: US20230057564A1
- Status: Published application
- Kind: A1
- Application number: 18/045,155
- Filed: 2022-10-08
- Published: 2023-02-23
- Earliest claimed: 2022-08-30
- Assignees: Chengdu Qinchuan Iot Technology Co., Ltd.
- Applicants: Chengdu Qinchuan Iot Technology Co., Ltd.
- Inventors: Zehua SHAO; Yuefei WU; Junyan ZHOU; Yaqiang QUAN; Xiaojun WEI
- Web page: https://patentgrep.com/patent/US20230057564A1

## CPC

- G01C 21/3407: {specially adapted for specific applications}
- G01C 21/3602: {Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera}
- G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202)
- G06V 10/70: using pattern recognition or machine learning (optical pattern recognition or electronic computations therefor G06V10/88)
- G06V 10/806: {of extracted features}
- G06V 20/10: Terrestrial scenes (scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59)
- G06V 20/50: Context or environment of the image
- G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69)
- G06V 10/82: using neural networks
- G16Y 40/60: Positioning; Navigation
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- 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
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16Y: INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]

## Abstract

The disclosure provides a method for managing a cleaning route in a smart city, an Internet of Things system and storage medium. The method includes: obtaining target information of a target area within a preset time period based on an object platform; sending the target information to a management platform through a sensor network platform; determining a cleaning route of the target area by processing the target information of the target area based on the management platform, including: determining an estimated amount of fallen leaves of each section of road in the target area; determining an estimated falling range of fallen leaves of each section of road in the target area; determining a cleaning difficulty evaluation value of each section of road based on the estimated amount of fallen leaves and the estimated falling range; and determining a cleaning route based on the cleaning difficulty evaluation value.

## Claims

### Claim 1

A method for managing a cleaning route in a smart city, implemented based on an Internet of Things system for managing the cleaning route in the smart city, wherein the Internet of Things system for managing the cleaning route in the smart city includes a management platform, a sensor network platform, and an object platform; the method including:
obtaining target information of a target area within a preset time period based on the object platform, wherein the target information includes at least one of road monitoring information, weather information, and sidewalk tree information;
sending the target information of the target area to the management platform through the sensor network platform;
determining a cleaning route of the target area by processing the target information of the target area based on the management platform, including:
determining an estimated amount of fallen leaves of each section of road in the target area according to the target information;
determining an estimated falling range of fallen leaves of each section of road in the target area according to the target information and the estimated amount of fallen leaves;
determining a cleaning difficulty evaluation value of each section of road based on the estimated amount of fallen leaves and the estimated falling range of each section of road; and
determining the cleaning route based on the cleaning difficulty evaluation value of each section of road.

### Claim 2 (depends on 1)

The method of claim 1, wherein the sensor network platform includes a plurality of sensor network sub-platforms corresponding to various target areas in a city, and the management platform includes a plurality of management sub-platforms corresponding to various target areas in the city;
for each target area in the city, the method including:
sending a target information obtaining instruction of a target area through a sensor network platform corresponding to the target area;
obtaining the target information of the target area within the preset time period based on the object platform in response to the target information obtaining instruction, and sending the target information of the target area to the sensor network sub-platform;
sending the target information of the target area to a management sub-platform corresponding to the target area through the sensor network platform; and
determining the cleaning route of the target area by processing the target information of the target area based on the management sub-platform.

### Claim 3 (depends on 2)

The method of claim 2, wherein the Internet of Things system for managing the cleaning route in the smart city also includes a service platform and a user platform, the service platform includes a plurality of service sub-platforms corresponding to all the management sub-platforms and a general database for summarizing and processing data of the plurality of service sub-platforms;
for each target area in the city, the method also including:
sending the cleaning route of the target area to the service sub-platform corresponding to the management sub-platform through the management sub-platform corresponding to the target area;
receiving the cleaning route of the target area through the service sub-platform, and summarizing the cleaning route of the target area to the general database; and
obtaining the cleaning route of the city by summarizing the cleaning route of each target area in the city based on the general database, and sending the cleaning route of the city to the user platform.

### Claim 4 (depends on 1)

The method of claim 1, wherein the obtaining target information of a target area within a preset time period based on the object platform includes:
determining a sidewalk tree image based on the road monitoring information; and
determining the sidewalk tree information by processing the image of the sidewalk trees based on an image recognition model, wherein the image recognition model is a machine learning model.

### Claim 5 (depends on 1)

The method of claim 1, wherein the determining an estimated amount of fallen leaves of each section of road in the target area according to the target information includes:
determining an amount of fallen leaves of each section of road in the target area by processing the target information based on an estimation model for the amount of fallen leaves, wherein the estimation model for the amount of fallen leaves is a machine learning model.

### Claim 6 (depends on 1)

The method of claim 1, wherein the determining an estimated falling range of fallen leaves of each section of the road in the target area according to the target information and the estimated amount of fallen leaves includes:
determining an estimated falling range of fallen leaves of each section of road in the target area by processing the target information and the estimated amount of fallen leaves based on a range estimation model, wherein the range estimation model is a machine learning model.

### Claim 7 (depends on 1)

The method of claim 1, wherein the determining a cleaning route based on a cleaning difficulty evaluation value of each section of road includes:
determining a preliminary planning route based on the cleaning difficulty evaluation value of each section of road;
obtaining an optimized cleaning route by optimizing the preliminary planning route based on a preset optimization algorithm;
determining whether the optimized cleaning route meets a preset requirement;
in response to a determination that the optimized cleaning route meets the preset requirement, using the optimized cleaning route as a final cleaning route; and
in response to a determination that the optimized cleaning route does not meet the preset requirement, optimizing the optimized cleaning route again based on the preset optimization algorithm.

### Claim 8 (depends on 7)

The method of claim 7, wherein the determining a preliminary planning route based on the cleaning difficulty evaluation value of each section of road includes:
obtaining basic road information of each section of road and traffic conditions between sections of road;
determining a unified cleaning score between the sections of road according to the traffic conditions between the sections of road and the cleaning difficulty evaluation value of each section of the road; and
determining the preliminary planning route by constructing an area planning diagram according to the basic road information and the cleaning difficulty evaluation value of each section of road and the unified cleaning score between the sections of the road, including:
determining each node of the area planning diagram according to the basic road information and the cleaning difficulty evaluation value of each section of road;
constructing edges between the corresponding nodes in the area planning diagram according to a connection relationship between the sections of road;
using the unified cleaning score between the sections of road as an edge score of a corresponding edge; and
determining the preliminary planning route according to the edges in the area planning diagram and the corresponding edge scores.

### Claim 9 (depends on 8)

The method of claim 8, wherein the determining whether the optimized cleaning route meets a preset requirement includes:
determining a number of routes in the optimized cleaning route, wherein the routes refer to a set of edges connecting a plurality of nodes consecutively in the area planning diagram;
determining whether the number of the routes is less than a first preset threshold;
in response to a determination that the number of the routes is less than the first preset threshold, determining that the optimized cleaning route meets the preset requirement; or
determining whether a number of the edges is less than a second preset threshold; and
in response to a determination that the number of the edges is less than the second preset threshold, determining that the optimized cleaning route meets the preset requirement.

### Claim 10 (depends on 8)

The method of claim 8, wherein the obtaining an optimized cleaning route by optimizing the preliminary planning route based on a preset optimization algorithm includes:
updating an edge score in each piece of edge information according to a preset rule;
removing the edge whose updated edge score is less than a preset threshold in the area planning diagram; and
determining the optimized cleaning route according to the updated edges and the updated edge scores in the area planning diagram.

### Claim 11

An Internet of Things system for managing a cleaning route in a smart city, including a management platform, a sensor network platform, and an object platform, wherein
the object platform is configured to obtain target information of a target area within the preset time period, wherein the target information includes at least one of road monitoring information, weather information, and sidewalk tree information;
the sensor network platform is configured to send the target information of the target area to the management platform;
the management platform is configured to determine a cleaning route of the target area by processing the target information of the target area, wherein the management platform is further configured to:
determine an estimated amount of fallen leaves of each section of road in the target area according to the target information;
determine an estimated falling range of fallen leaves of each section of road in the target area according to the target information and the estimated amount of fallen leaves;
determine a cleaning difficulty evaluation value of each section of road based on the estimated amount of fallen leaves and the estimated falling range of each section of the road; and
determine the cleaning route based on the cleaning difficulty evaluation value of each section of road.

### Claim 12 (depends on 11)

The system of claim 11, wherein the sensor network platform includes a plurality of sensor network sub-platforms corresponding to various target areas in a city, and the management platform includes a plurality of management sub-platforms corresponding to various target areas in the city;
each sensor network sub-platform in the plurality of sensor network sub-platforms is configured to send a target information obtaining instruction of a target area to the object platform, obtain target information of the target area within a preset time period through the object platform, and send the target information to a management sub-platform corresponding to the target area; and
each management sub-platform of the plurality of management sub-platforms is configured to process the target information of the target area to determine the cleaning route of the target area.

### Claim 13 (depends on 12)

The system of claim 12, wherein the Internet of Things system for managing the cleaning route in the smart city further comprises a service platform and a user platform, the service platform includes a plurality of service sub-platforms corresponding to all the management sub-platforms and a general database for summarizing and processing data of the plurality of service sub-platforms;
each management sub-platform in the plurality of management sub-platforms is further configured to send the cleaning route of the target area to a service sub-platform corresponding to the management sub-platform;
each service sub-platform of the plurality of service sub-platforms is configured to receive the cleaning route of the target area sent by the management sub-platform, and summarize the cleaning route of the target area into the general database; and
the general database is configured to summarize the cleaning route of each target area in the city to obtain the cleaning route of the city, and send the cleaning route to the user platform.

### Claim 14 (depends on 11)

The system of claim 11, wherein the object platform is further configured to:
determine a sidewalk tree image based on the road monitoring information; and
determine the sidewalk tree information by processing the sidewalk tree image based on an image recognition model, wherein the image recognition model is a machine learning model.

### Claim 15 (depends on 11)

The system of claim 11, wherein the management platform is further configured to:
determine an amount of fallen leaves of each section of road in the target area by processing the target information based on an estimation model for an amount of fallen leaves, wherein the estimation model for the amount of fallen leaves is a machine learning model.

### Claim 16 (depends on 11)

The system of claim 11, wherein the management platform is further configured to:
determine an estimated range of fallen leaves of each section of road in the target area by processing the target information and the estimated amount of fallen leaves based on a range estimation model, wherein the range estimation model is a machine learning model.

### Claim 17 (depends on 11)

The system of claim 11, wherein the management platform is further configured to:
determine a preliminary planning route based on the cleaning difficulty evaluation value of each section of road;
obtain an optimized cleaning route by optimizing the preliminary planning route based on a preset optimization algorithm;
determine whether the optimized cleaning route meets a preset requirement;
in response to a determination that the optimized cleaning route meets the preset requirement, use the optimized cleaning route as a final cleaning route; and
in response to a determination that the optimized cleaning route does not meet the preset requirement, optimize the optimized cleaning route again based on the preset optimization algorithm.

### Claim 18 (depends on 17)

The system of claim 17, wherein the management platform is further configured to:
obtain basic road information of each section of road and traffic conditions between sections of road;
determine a unified cleaning score between the sections of road according to the traffic conditions between the sections of road and the cleaning difficulty evaluation value of each section of the road; and
determine the preliminary planning route by constructing an area planning diagram according to the basic road information and the cleaning difficulty evaluation value of each section of road and the unified cleaning score between the sections of the road, including
determining each node of the area planning diagram according to the basic road information and the cleaning difficulty evaluation value of each section of road;
constructing edges between the corresponding nodes in the area planning diagram according to a connection relationship between the sections of road;
using the unified cleaning score between the sections of road as an edge score of a corresponding edge; and
determining the preliminary planning route according to the edges in the area planning diagram and the corresponding edge scores.

### Claim 19 (depends on 18)

The system of claim 18, wherein the management platform is further configured to:
determine a number of routes in the optimized cleaning route, wherein the routes refer to a set of edges connecting a plurality of nodes consecutively in the area planning diagram;
determine whether the number of the routes is less than a first preset threshold;
in response to a determination that the number of the routes is less than the first preset threshold, determine that the optimized cleaning route meets the preset requirement; or
determine whether the number of the edges is less than a second preset threshold; and
in response to a determination that the number of the edges is less than the second preset threshold, determine that the optimized cleaning route meets the preset requirement.

### Claim 20 (depends on 1)

A non-transitory computer readable storage medium, wherein the storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method of claim 1 is implemented.