# US 12,112,630 B2 · Methods for recommending parking lots in smart cities, internet of things systems, and storage medium thereof

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- Number: US 12,112,630 B2
- Document ID: US12112630B2
- Status: Granted
- Kind: B2
- Application number: 18/048,516
- Filed: 2022-10-21
- Published: 2023-03-02
- Granted: 2024-10-08
- Earliest claimed: 2022-10-14
- Assignees: Chengdu Qinchuan Iot Technology Co., Ltd.
- Applicants: Chengdu Qinchuan Iot Technology Co., Ltd.
- Inventors: Zehua Shao; Haitang Xiang; Yaqiang Quan; Yong Li; Xiaojun Wei
- Web page: https://patentgrep.com/patent/US12112630B2

## CPC

- G08G 1/141: {with means giving the indication of available parking spaces}
- G08G 1/144: {on portable or mobile units, e.g. personal digital assistant [PDA]}
- G06Q 10/028: {for seating or spaces in a venue}
- G08G 1/0129: {for creating historical data or processing based on historical data}
- G08G 1/146: {where the parking area is a limited parking space, e.g. parking garage, restricted space}
- G08G 1/148: {Management of a network of parking areas}
- G16Y 20/10: relating to the environment, e.g. temperature; relating to location
- G16Y 40/10: Detection; Monitoring
- G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93})
- 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
- G16Y: INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]

## Abstract

The embodiments of the present disclosure provide a method for recommending a parking lot in a smart city and an Internet of Things system, implemented based on a management platform of an Internet of Things system for recommending a parking lot in a smart city. The method includes: determining an arrival duration of a user reaching at least one candidate parking lot and a prediction time period corresponding to each candidate parking lot in the at least one candidate parking lot based on a user request; determining an occupation rate of the candidate parking lot during the prediction time period; and determining a recommended parking lot at least based on the occupation rate of the candidate parking lot during the prediction time period and parking lot information of the candidate parking lot.

## Claims

### Claim 1

A method for recommending a parking lot in a smart city, implemented based on an Internet of Things system for recommending the parking lot in the smart city, including:
determining an arrival duration of a user reaching at least one candidate parking lot and a prediction time period corresponding to each candidate parking lot in the at least one candidate parking lot based on a user request;
determining an occupation rate of the candidate parking lot during the prediction time period; and
determining a recommended parking lot at least based on the occupation rate of the candidate parking lot during the prediction time period and parking lot information of the candidate parking lot;
wherein the parking lot information further includes at least one of a historical occupation rate of the parking lot, a parking lot capacity, and surrounding event holding information; and
the determining the occupation rate of the candidate parking lot during the prediction time period includes:
constructing a parking lot feature vector corresponding to the candidate parking lot based on the parking lot capacity of the candidate parking lot, an internal structure of the parking lot, and the surrounding event holding information during the prediction time period;
obtaining a reference historical vector through retrieving in a vector database based on the parking lot feature vector; and
using the historical occupation rate of the parking lot corresponding to the reference historical vector as the occupation rate of the candidate parking lot during the prediction time period.

### Claim 2 (depends on 1)

The method of claim 1, wherein the Internet of Things system for recommending the parking lot in the smart city also includes a user platform, a management platform, a service platform, a sensor network platform, and an object platform;
the management platform includes a general database and a plurality of management sub-platforms;
the sensor network platform includes a plurality of sensor network sub-platforms;
the user request is obtained based on the user platform, and transmitted to the general database of the management platform based on the service platform;
the parking lot information is obtained based on the object platform, transmitted to the corresponding management sub-platforms based on the corresponding sensor network sub-platforms, and uploaded to the general database by the management sub-platforms; and
the method further includes:
transmitting the recommended parking lot to the user platform based on the service platform.

### Claim 3 (depends on 1)

The method of claim 1, wherein the user request includes at least one of a current vehicle location, an estimated departure time, and an estimated stay time;
the prediction time period includes a time period of time when the user arrives at the candidate parking lot or a time period of time when the user leaves the candidate parking lot; and
the prediction time period is determined based on the arrival duration of the user reaching the candidate parking lot and the user request.

### Claim 4 (depends on 3)

The method of claim 3, wherein the parking lot information includes at least one of an internal structure of the parking lot and a charging standard;
the determining the recommended parking lot includes:
determining the recommended parking lot at least based on the occupation rate of the candidate parking lot during the prediction time period, the internal structure of the candidate parking lot, the charging standard, and the arrival duration of the user reaching the candidate parking lot.

### Claim 5 (depends on 1)

The method of claim 1, wherein the parking lot information further includes a historical number of entry and exit vehicles of the candidate parking lot; and
the determining the occupation rate of the candidate parking lot during the prediction time period further includes:
verifying the occupation rate of the candidate parking lot during the prediction time period based on the historical number of entry and exit vehicles corresponding to the candidate parking lot within a first historical time period; and
determining a final occupation rate of the candidate parking lot during the prediction time period based on a verification result.

### Claim 6 (depends on 5)

The method of claim 5, wherein the verifying the occupation rate of the candidate parking lot during the prediction time period based on the historical number of entry and exit vehicle corresponding to the candidate parking lot within a first historical time period includes:
obtaining the historical number of entry and exit vehicle corresponding to the candidate parking lot within the first historical time period, and constructing a vehicle flow feature vector corresponding to the first historical time period;
predicting a predicted number of entry and exit vehicle corresponding to a second historical time period through conducting vector retrieval in a historical database based on the vehicle flow feature vector; and
determining whether a predicted occupation rate of the candidate parking lot during the prediction time period is reasonable based on a difference between the predicted number of entry and exit vehicle within the second historical time period and an actual number of entry and exit vehicle within the second historical time period;
the determining the final occupation rate of the candidate parking lot during the prediction time period based on the verification result includes:
in response to a determination that the predicted occupation rate of the candidate parking lot during the prediction time period is reasonable, using the predicted occupation rate of the candidate parking lot during the prediction time period as the final occupation rate; and
in response to a determination that the predicted occupation rate of the candidate parking lot during the prediction time period is not reasonable, adjusting the predicted occupation rate of the candidate parking lot during the prediction time period, and using the adjusted occupation rate as the final occupation rate.

### Claim 7 (depends on 1)

The method of claim 1, wherein the determining a recommended parking lot at least based on the occupation rate of the candidate parking lot during the prediction time period and parking lot information of the candidate parking lot includes:
determining at least one evaluation factor and a score corresponding to each evaluation factor in the at least one evaluation factor based on the occupation rate of the candidate parking lot during the prediction time period and the parking lot information of the candidate parking lot;
determining a weight value corresponding to each evaluation factor, and determining a score of the candidate parking lot based on the weight value of each evaluation factor and the score of each evaluation factor; and
determining the recommended parking lot based on the score of the candidate parking lot.

### Claim 8 (depends on 7)

The method of claim 7, wherein the at least one evaluation factor includes: at least one of the occupation rate of the candidate parking lot during the prediction time period, an internal structure of the candidate parking lot, a charging standard, and the arrival duration.

### Claim 9 (depends on 1)

The method of claim 1, wherein the determining a recommended parking lot at least based on the occupation rate of the candidate parking lot during the prediction time period and parking lot information of the candidate parking lot includes:
determining a score of the candidate parking lot through processing parking lot features, candidate parking lot features, and user features historically selected by the user through a score prediction model, wherein the score prediction model is a machine learning model;
determining a first recommendation based on the score of the candidate parking lot; and
determining a second recommendation in the first recommendation as the recommended parking lot in combination with the occupation rate of the candidate parking lot during the prediction time period and the corresponding arrival duration in the first recommendation.

### Claim 10

An Internet of Things system for recommending a parking lot in a smart city, including a management platform, wherein the management platform is configured to:
determine an arrival duration of a user reaching at least one candidate parking lot and a prediction time period corresponding to each candidate parking lot in at least one candidate parking lot based on a user request;
determine an occupation rate of the candidate parking lot during the prediction time period; and
determine a recommended parking lot at least based on the occupation rate of the candidate parking lot during the prediction time period and parking lot information of the candidate parking lot;
wherein the parking lot information further includes at least one of a historical occupation rate of the parking lot, a parking lot capacity, and surrounding event holding information; and
the management platform is further configured to:
construct a parking lot feature vector corresponding to the candidate parking lot based on the parking lot capacity of the candidate parking lot, an internal structure of the parking lot, and surrounding event holding information during the prediction time period;
obtain a reference historical vector through retrieving in a vector database based on the parking lot feature vector; and
use the historical occupation rate of the parking lot corresponding to the reference historical vector as the occupation rate of the candidate parking lot during the prediction time period.

### Claim 11 (depends on 10)

The system of claim 10, wherein the system further includes a user platform, a management platform, a service platform, a sensor network platform, and an object platform;
the management platform includes a general database and a plurality of management sub-platforms;
the sensor network platform includes a plurality of sensor network sub-platforms;
the user request is obtained based on the user platform, and transmitted to the general database of the management platform by the service platform;
the parking lot information is obtained based on the object platform, transmitted to the corresponding management sub-platforms based on the corresponding sensor network sub-platforms, and uploaded to the general database by the management sub-platforms; and
the service platform transmits the recommended parking lot to the user platform.

### Claim 12 (depends on 10)

The system of claim 10, wherein the user request includes at least one of a current vehicle location, an estimated departure time, and an estimated stay time;
the prediction time period includes a time period of time when the user reaches the candidate parking lot or a time period of time when the user leaves the candidate parking lot; and
the prediction time period is determined based on the arrival duration of the user reaching the candidate parking lot and the user request.

### Claim 13 (depends on 12)

The system of claim 12, wherein the parking lot information includes at least one of an internal structure of the parking lot and a charging standard;
the management platform is further configured to:
determine the recommended parking lot at least based on the occupation rate of the candidate parking lot during the prediction time period, the internal structure of the candidate parking lot, the charging standard, and the arrival duration of the user reaching the candidate parking lot.

### Claim 14 (depends on 10)

The system of claim 10, wherein the parking lot information further includes a historical number of entry and exit vehicles of the candidate parking lot; and
the management platform is further configured to:
verify the occupation rate of the candidate parking lot during the prediction time period based on the historical number of entry and exit vehicles corresponding to the candidate parking lot within a first historical time period; and
determine a final occupation rate of the candidate parking lot during the prediction time period based on a verification result.

### Claim 15 (depends on 14)

The system of claim 14, wherein the management platform is further configured to:
obtain the historical number of entry and exit vehicles corresponding to the candidate parking lot within the first historical time period, and construct a vehicle flow feature vector corresponding to the first historical time period;
predict a predicted number of entry and exit vehicles corresponding to a second historical time period through conducting vector retrieval in a historical database based on the vehicle flow feature vector;
determine whether a predicted occupation rate of the candidate parking lot during the prediction time period is reasonable based on a difference between the predicted number of entry and exit vehicles within the second historical time period and an actual number of entry and exit vehicles within the second historical time period;
in response to a determination that the predicted occupation rate of the candidate parking lot during the prediction time period is reasonable, use the predicted occupation rate of the candidate parking lot during the prediction time period as the final occupation rate; and
in response to a determination that the predicted occupation rate of the candidate parking lot during the prediction time period is not reasonable, adjust the predicted occupation rate of the candidate parking lot during the prediction time period, and use the adjusted occupation rate as the final occupation rate.

### Claim 16 (depends on 10)

The system of claim 10, wherein the management platform is further configured to:
determine at least one evaluation factor and a score corresponding to each evaluation factor in the at least one evaluation factor based on the occupation rate of the candidate parking lot during the prediction time period and the parking lot information of the candidate parking lot;
determine a weight value corresponding to each evaluation factor, and determine a score of the candidate parking lot based on the weight value of each evaluation factor and the score of each evaluation factor; and
determine the recommended parking lot based on the score of the candidate parking lot.

### Claim 17 (depends on 10)

The system of claim 10, wherein the management platform is further configured to:
determine a score of the candidate parking lot through processing parking lot features, candidate parking lot features, and user features historically selected by the user through a score prediction model, wherein the score prediction model is a machine learning model;
determine a first recommendation based on the score of the candidate parking lot; and
determine a second recommendation in the first recommendation as the recommended parking lot in combination with the occupation rate of the candidate parking lot during the prediction time period and the corresponding arrival duration in the first recommendation.

### Claim 18

A non-transitory computer readable storage medium, wherein the storage medium stores computer instructions, and when a computer reads the computer instructions, the computer executes a method for recommending a parking lot in a smart city, including:
determining an arrival duration of a user reaching at least one candidate parking lot and a prediction time period corresponding to each candidate parking lot in the at least one candidate parking lot based on a user request;
determining an occupation rate of the candidate parking lot during the prediction time period; and
determining a recommended parking lot at least based on the occupation rate of the candidate parking lot during the prediction time period and parking lot information of the candidate parking lot;
wherein the parking lot information further includes at least one of a historical occupation rate of the parking lot, a parking lot capacity, and surrounding event holding information; and
the determining the occupation rate of the candidate parking lot during the prediction time period includes:
constructing a parking lot feature vector corresponding to the candidate parking lot based on the parking lot capacity of the candidate parking lot, an internal structure of the parking lot, and the surrounding event holding information during the prediction time period;
obtaining a reference historical vector through retrieving in a vector database based on the parking lot feature vector; and
using the historical occupation rate of the parking lot corresponding to the reference historical vector as the occupation rate of the candidate parking lot during the prediction time period.