CN-121982890-A - Scenic spot movement balance car management method, device and medium based on cloud computing

CN121982890ACN 121982890 ACN121982890 ACN 121982890ACN-121982890-A

Abstract

The invention discloses a scenic spot movement balance car management method, equipment and medium based on cloud computing, and relates to the technical field of cloud computing, comprising the steps of constructing an enhanced scenic spot rule base containing road sections and parking rules, computing the dynamic type of an area where a movement balance car is located based on the enhanced scenic spot rule base, and performing admission control of a user leasing request according to the dynamic type; the method comprises the steps of collecting running data of a motion balance car in real time to judge the intention of returning the car, matching a recommended parking area through a judging result, calculating a congestion index of the recommended parking area based on the fact that the counting of in-out events is combined with virtual calibration and parking state identification, screening the recommended parking area by using the congestion index, generating a control strategy, and issuing and pushing the control strategy. Through geofence event calibration, multidimensional congestion evaluation and SVDD health diagnosis, dynamic closed loop of scenic spot movement balance car management is realized, and scenic spot operation management safety, resource utilization rate and user satisfaction are remarkably improved.

Inventors

LV WEICHAO
SHI YONGGUANG
LV LIFANG
ZHOU XIAOFEI

Assignees

永康市堂胜工贸有限公司

Dates

Publication Date: 20260505
Application Date: 20260126

Claims (10)

1. A scenic spot movement balance car management method based on cloud computing is characterized by comprising the following steps of, Constructing an enhanced scenic spot rule base containing road sections and parking rules, calculating the dynamic type of the area where the motion balance car is located based on the enhanced scenic spot rule base, and performing access control of a user leasing request according to the dynamic type; Collecting running data of the motion balance car in real time to judge the intention of returning the car, matching a recommended parking area through a judging result, and calculating a congestion index of the recommended parking area based on the in-out event count and the virtual calibration and parking state identification; And screening a recommended parking area by using the congestion index, generating a control strategy, issuing and pushing the control strategy, evaluating running data of the motion balance car, and performing intelligent operation and maintenance scheduling based on an evaluation result.
2. The scenic spot movement balance car management method based on cloud computing as recited in claim 1, wherein the real-time acquisition of movement balance car operation data for car returning intention judgment means that a cloud management platform creates a car session record when renting starts, and meanwhile, the state of a car is updated to be in use, in the running process of the car, an MCU periodically reads the measurement result of a wheel speed sensor, calculates the current car speed for speed limiting control and for judging the riding intention, and when the car meets the riding ending preset condition, reports the latest positioning coordinates of the car.
3. The scenic spot movement balance car management method based on cloud computing, which is characterized in that the scenic spot movement balance car management method based on cloud computing is characterized in that a judgment result is matched with a recommended parking area, virtual calibration and parking state recognition are combined based on in-out event counting, the calculation of the congestion index of the recommended parking area refers to the construction of a circular query area by taking a positioning coordinate as a circle center and a fixed search radius after the cloud management platform receives the latest positioning coordinate reported by the movement balance car, the geofence with a space intersection relation with the circular query area is searched in an enhanced scenic spot rule base, the geographical fences meeting the 'regional type' as 'parkable' and the 'dynamic type' as 'normal' are reserved from the search result, a recommended parking area set is formed, if the parking area set is empty, no suitable recommended parking point is indicated nearby, no parking recommendation is generated this time, and otherwise, the congestion index of each recommended parking area is calculated.
4. The method for scenic spot movement balance car management based on cloud computing as recited in claim 3, wherein the step of screening the recommended parking areas by using the congestion index and generating the control strategy is to screen a candidate parking area set by using the congestion index, select the recommended parking areas, write the identification of the recommended parking areas into the control strategy of the car, record the identification parameters of the recommended parking areas, and generate the corresponding riding control strategy by combining the maximum allowable running speed of the dynamic type.
5. The method for managing the scenic spot movement balance car based on cloud computing as recited in claim 4, wherein the issuing and pushing of the control strategy means that the cloud management platform issues the riding control strategy to the vehicle-mounted control unit of the corresponding car, and the riding control strategy is pushed to the mobile terminal of the user after the strategy is successfully issued.
6. The method for managing the scenic spot movement balance car based on cloud computing, which is characterized by comprising the steps of evaluating movement balance car operation data, carrying out intelligent operation and maintenance scheduling based on an evaluation result, namely receiving real-time operation data packets reported by the movement balance car, forming a historical operation data set, processing the historical operation data set, computing total acceleration, computing speed intensity characteristics, braking intensity characteristics and low-speed driving intensity characteristics based on the speed and total acceleration, forming a six-dimensional health feature vector training support vector data description model by combining average instantaneous power consumption, working current standard deviation and tire air pressure, determining a model super-parameter through Bayesian optimization, constructing current six-dimensional health feature vectors for newly received real-time operation data, inputting a trained SVDD model, judging whether the car is in an abnormal state, further executing a deterministic multi-evidence reason labeling flow for the car judged to be abnormal, marking the car as a car to be scheduled based on labeling information, generating a maintenance scheduling task record for each car based on main reasons of triggering scheduling, carrying out updating the task in a terminal maintenance terminal of the scheduling, updating the generated scheduling task, carrying out the task in a terminal maintenance circulation and updating state of the current operation and the running state of the car, and carrying out continuous updating and the task, and carrying out the updating and the task maintenance monitoring and the running state of the terminal maintenance terminal after the current and the task is continuously updated and the task is completed.
7. The scenic spot movement balance car management method based on cloud computing, wherein the dynamic type of the area where the movement balance car is located is calculated based on an enhanced scenic spot rule base, admission control of a user lease request is carried out according to the dynamic type, a tourist initiates the lease request by scanning a two-dimensional code of a vehicle through a mobile terminal, the mobile terminal sends unique identification of the vehicle and the unique identification of the user to a cloud management platform, positioning coordinates which are reported last time by the vehicle are inquired, space matching is carried out through a road section set containing static management attributes, a road section to which the movement balance car belongs is determined, a mapping relation obtained by the static attributes corresponding to the road section is read, an environment context data set containing environment and people flow information is formed, real-time matching is carried out based on the mapping relation, the environment context data set and the dynamic adjustment rule, the dynamic type of the area where the vehicle is located is obtained, whether the current area where the vehicle is suitable for initiating riding is judged based on the dynamic type of the area where the vehicle is located, user qualification verification is carried out, and the corresponding maximum allowed running speed is obtained after the user qualification verification is passed.
8. The method for managing the scenic spot movement balance car based on cloud computing as recited in claim 7, wherein the constructing of the enhanced scenic spot rule base containing road sections and parking rules refers to importing scenic spot electronic map data adopting a unified coordinate system into a cloud map database, initializing and constructing a rule base, creating an initial road section set in the rule base, sequentially carrying out attribute configuration on each road section in the initial road section set to form a road section set containing static management attributes, screening out road sections with all regional types of 'parkable' as parking areas, verifying the maximum number of accommodated vehicles, distributing parking area IDs into the rule base, forming a parking area capacity mapping set, configuring dynamic adjustment rules through the road section set containing static management attributes and the parking area capacity mapping set, configuring corresponding maximum allowable driving speeds, forming a speed mapping table, and constructing the enhanced scenic spot rule base by combining geo-fence boundary data and regional path attribute data.
9. A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the computer program is characterized in that the processor realizes the steps of the scenic spot movement balance car management method, the scenic spot movement balance car management device and the medium based on cloud computing as set forth in any one of claims 1 to 8 when executing the computer program.
10. A computer readable storage medium is provided with a computer program, which is characterized in that the computer program is executed by a processor to realize the steps of the scenic spot movement balance car management method, the scenic spot movement balance car management device and the medium based on cloud computing as set forth in any one of claims 1 to 8.

Description

Scenic spot movement balance car management method, device and medium based on cloud computing Technical Field The invention relates to the technical field of cloud computing, in particular to a scenic spot movement balance car management method, device and medium based on cloud computing. Background With the development of intelligent travel and shared travel, more and more scenic spots begin to use small-sized walking tools such as electric balance cars. Most of the management systems at present use the internet of things and map technology to realize vehicle positioning and basic scheduling, some of the management systems also define parking areas through electronic fences, and the management systems are combined with a simple parking space counting or user reporting mode to roughly know whether parking spots are full. In recent years, with the improvement of cloud computing capability, the system can support large-scale equipment collaborative management, and can also process data in real time and remotely issue scheduling instructions. However, the existing management method has obvious defects, firstly, the congestion evaluation dimension is single, influence of scenic spot specific factors such as terrain height difference, walking distance and the like on parking will and turnover efficiency of users is ignored, and accurate guiding strategies are difficult to generate. Secondly, the vehicle health state depends on periodic manual inspection, and on-line abnormality detection and root cause marking capability based on multi-source operation data is lacking, so that operation and maintenance response is lagged. Therefore, the existing scenic spot movement balance car management has the problems of inaccurate parking guidance, distorted resource assessment and passive inefficiency of operation and maintenance. Disclosure of Invention The present invention has been made in view of the above-described problems occurring in the prior art. Therefore, the invention provides a scenic spot movement balance car management method, equipment and medium based on cloud computing, which solve the problems of inaccurate parking guidance, resource assessment distortion and passive inefficiency of operation and maintenance. In order to solve the technical problems, the invention provides the following technical scheme: In a first aspect, the invention provides a scenic spot movement balance car management method based on cloud computing, which comprises the following steps of, Constructing an enhanced scenic spot rule base containing road sections and parking rules, calculating the dynamic type of the area where the motion balance car is located based on the enhanced scenic spot rule base, and performing access control of a user leasing request according to the dynamic type; Collecting running data of the motion balance car in real time to judge the intention of returning the car, matching a recommended parking area through a judging result, and calculating a congestion index of the recommended parking area based on the in-out event count and the virtual calibration and parking state identification; And screening a recommended parking area by using the congestion index, generating a control strategy, issuing and pushing the control strategy, evaluating running data of the motion balance car, and performing intelligent operation and maintenance scheduling based on an evaluation result. The cloud computing-based scenic spot movement balance car management method comprises the steps of collecting movement balance car operation data in real time to judge the intention of returning to the scenic spot, creating a car session record when renting starts by a cloud management platform, updating the state of a car to be in use, periodically reading the measurement result of a wheel speed sensor by an MCU (micro controller unit) in the running process of the car, calculating the current car speed to carry out speed limiting control and judging the intention of ending riding, and reporting the latest positioning coordinate of the car when the car meets the preset condition of ending riding. According to the scenic spot movement balance car management method based on cloud computing, a judgment result is matched with a recommended parking area, virtual calibration and parking state recognition are combined based on in-out event counting, the calculation of the congestion index of the recommended parking area refers to the construction of a circular query area by taking the positioning coordinates as the circle center and a fixed search radius after the cloud management platform receives the latest positioning coordinates reported by the movement balance car, a geofence with a space intersection relation with the circular query area is searched in an enhanced scenic spot rule base, the geofences meeting the 'regional type' as 'parkable' and the 'dynamic type' as 'normal' are reserved from the search result, a recommended parking a