CN116580545A - Standard parking method and system for sharing bicycle based on electronic fence - Google Patents
Standard parking method and system for sharing bicycle based on electronic fence Download PDFInfo
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- CN116580545A CN116580545A CN202310382873.2A CN202310382873A CN116580545A CN 116580545 A CN116580545 A CN 116580545A CN 202310382873 A CN202310382873 A CN 202310382873A CN 116580545 A CN116580545 A CN 116580545A
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Abstract
The invention discloses a sharing bicycle standard parking method and system based on an electronic fence, comprising the following steps of sharing bicycle electronic fence planning: in combination with an improved K-means algorithm, in order to find a proper position of the shared bicycle electronic fence, the distance between the shared bicycle parking demand points is utilized to cluster the shared bicycle parking demand points, the points with similar distances are marked into the same cluster, the central point position of the cluster is the electronic fence position, and the size of the cluster determines the fence size; setting the capacity of the shared bicycle electronic fence: determining the actual capacity of each electronic fence according to the total number of vehicles, parking requirements and the actual available area size of the electronic fence of the shared bicycle; e-fence parking management and guiding: and the shared bicycle standard parking is realized by combining the shared bicycle intelligent operation management service system. The method enables the shared bicycle electronic fence planning to be more fit with the parking requirement, effectively solves the problems of disordered parking, excessive stacking, road occupation and the like, and improves the rationality of the electronic fence planning.
Description
Technical Field
The invention relates to the technical field of shared bicycle management, in particular to a shared bicycle standard parking method and system based on an electronic fence.
Background
The current sharing single car becomes one of the mainstream modes of public transportation transfer and short distance trip, has effectively solved trip "last kilometer" problem, provides convenience for people's trip, but along with the continuous increase and the not enough of management of vehicle, sharing single car is in disorder to put, is stacked in excess, is crowded the road and has become a universal phenomenon, has seriously influenced city slow-going trip environment, has caused the waste of resource, has increased the administrative cost of operation enterprise. In order to scientifically and normally park a shared bicycle, the technology of an electronic fence is proposed in China to strengthen normal management, the electronic fence utilizes the technologies of satellite positioning, bluetooth connection and the like to monitor the parking position of the shared bicycle in real time so as to judge whether the parking of a user meets the position requirement, but the electronic fence planning, the capacity of the shared bicycle electronic fence, the parking guidance of the electronic fence and the like are insufficient at present.
Disclosure of Invention
The invention aims to provide a shared bicycle standard parking method and system based on an electronic fence, which realize shared bicycle electronic fence planning, electronic fence capacity setting and real-time monitoring and shared bicycle parking standard management and guiding based on the electronic fence based on an improved K-means clustering algorithm.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a sharing bicycle standard parking method based on electronic fence comprises the following steps:
s1: sharing a bicycle electronic fence program: in combination with an improved K-means algorithm, in order to find a proper position of the shared bicycle electronic fence, the distance between the shared bicycle parking demand points is utilized to cluster the shared bicycle parking demand points, the points with similar distances are marked into the same cluster, the central point position of the cluster is the electronic fence position, and the size of the cluster determines the fence size;
s2: setting the capacity of the shared bicycle electronic fence: determining the actual capacity of each electronic fence according to the total number of vehicles, parking requirements and the actual available area size of the electronic fence of the shared bicycle;
s3: e-fence parking management and guiding: and the shared bicycle standard parking is realized by combining the shared bicycle intelligent operation management service system.
Further, the improved K-means algorithm comprises an initial cluster center selection method and a cluster number determination method, wherein the initial cluster center selection method comprises the following steps:
according to the positions of the data points and the regional density, calculating the distance between the data points by adopting a data point circumference radiation model, obtaining a radiation data set of each data point, and selecting the most representative data point as a clustering center;
setting a threshold value P according to the data point scale, and allowing fine adjustment in the data execution process; defining r as the radius of the data point circular radiation model, and taking typically 1/2 of the average of all data point distances, the distance between the data points is calculated using the following formula:
the above formula is a distance calculation formula of two data points i and j in n dimensions, and x and y respectively represent the data of the data points i and j in each dimension.
Further, the cluster number determining method comprises the following steps:
two new parameters are established at two levels inside each cluster and between each cluster of the clustering result: data aggregation degree and center distance, wherein the data aggregation degree is defined by the following formula:
in the above formula, N is the data point number, x j Data points, m i Is a clustering center;
the center distance is defined by the following formula:
d(m i ,m j ) Is cluster c i And c j Is the distance of the cluster center;
the two parameters D and C represent the similarity of data in the clusters and the dispersion degree among the clusters, so that a comprehensive evaluation index, namely a clustering effect index V (k), is formed;
V(k)＝(C-D)/(C+D)
the clustering effect index is an index for comprehensively evaluating the clustering effect aiming at a certain clustering number K, the closer the clustering effect index is to 1, the better the clustering effect is, the iteration step length is required to be set according to the data scale, the clustering effect indexes of all K values in the range are obtained, and therefore the optimal clustering number is selected.
Further, the specific steps in S1 are as follows:
s101: according to the parking demand point data scale, the iteration range of the clustering number K is given, the iteration step length is adaptively selected in the range, and an iteration set of K is obtained;
s102: the method comprises the steps that an improved K-means algorithm is adopted to gather the shared bicycle parking demand points into K types, and data aggregation degree D and center distance C are calculated, so that clustering effect indexes V (K) with different K values are obtained;
s103: selecting a K value with the clustering effect index closest to 1 as the number of the electronic fences;
s104: calculating the distance between each parking demand point by adopting a data point circumference radiation model, and determining K initial clustering centers;
s105: for any parking demand point, solving the distance from the parking demand point to K clustering centers, and dividing the parking point into a cluster which is closest to the parking demand point;
s106: updating the center point or the mass center position of each cluster by using a mean value method;
s107: and returning to S105 and S106 for the K updated clustering centers, if the central point position change is smaller after iteration, considering that the stable state is reached or the set iteration times are reached, and ending the iteration, namely outputting the positions of the K sharing bicycle electronic fences.
Further, the specific method in S3 is as follows:
s301: selecting each electronic fence on the electronic map of the shared bicycle intelligent operation management service system according to the four-point coordinate frame of the determined actual parking area;
s302: before riding, a user inquires the number of remaining space of the shared bicycle electronic fence near the destination through a mobile phone APP or a small program;
s303: when a user arrives at the electronic fence to prepare for parking the vehicle, the system judges whether the vehicle is in the range of the electronic fence according to the position data sent by the shared bicycle intelligent lock;
s304: if the vehicle is judged to be in the electronic fence and the number of remaining empty bits of the electronic fence is > =1, the user can smoothly lock the intelligent lock and finish vehicle parking; if the vehicle is judged not to be located in the electronic fence, the user is not allowed to finish intelligent locking, and the user can inquire the nearest parked electronic fence at the mobile phone end APP or the applet in real time to finish sharing bicycle parking.
The invention provides another technical scheme that: the utility model provides a sharing bicycle standard parking system based on electronic fence, includes cell-phone end APP or applet, sharing bicycle intelligent lock and sharing bicycle electronic fence; the mobile phone terminal APP or applet establishes data communication connection with the shared bicycle intelligent lock and the shared bicycle electronic fence respectively; the mobile phone terminal APP or the applet is used for displaying the position of the shared bicycle electronic fence in the area and the number of the shared bicycle in real time; and the sharing bicycle judges whether the bicycle is in the sharing bicycle electronic fence or not according to the position data sent by the intelligent lock.
Compared with the prior art, the invention has the beneficial effects that:
the sharing bicycle standard parking method and system based on the electronic fence provided by the invention are different from the problems that the electronic fence is lack of reasonable planning and insufficient guiding for parking by utilizing the electronic fence standard sharing bicycle in the prior art; the shared bicycle electronic fence planning is realized based on an improved K-means clustering algorithm, so that the shared bicycle electronic fence planning is more fit with the parking requirement; and setting the capacity of the electronic fence and monitoring in real time, realizing standard management and guidance of shared bicycle parking based on the electronic fence, promoting a user to efficiently and normally park the vehicle, and effectively solving the problems of random parking, excessive stacking, road occupation and the like. Meanwhile, the improved K-means clustering algorithm solves the problems that an initial clustering center of the existing algorithm is selected randomly and the clustering number must be specified in advance, the clustering effect is better, and therefore the rationality of electronic fence planning is improved.
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FIG. 1 is a flowchart of an initial cluster center selection method according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a sharing bicycle standard parking method based on an electronic fence, which comprises the following steps:
step one: shared bicycle electronic fence planning based on improved K-means clustering algorithm: aiming at the problems that the initial cluster center is randomly selected and the cluster number must be specified in advance in the traditional K-means clustering algorithm, the method improves the two aspects, and comprises the following steps:
1. the initial cluster center selection method comprises the following steps:
the ideal initial clustering center should select the most representative data point as the clustering center according to the position and the regional density of the data point, therefore, the distance between the data points is calculated by adopting the data point circumference radiation model, and the radiation data set of each data point is obtained, the method can integrate the data point distribution density calculation into the algorithm while controlling the clustering center distance, and the specific flow is shown in figure 1:
the threshold P mentioned in fig. 1 is set according to the data point size and allows fine tuning during data execution; r is the radius of the data point circumferential radiation model, and is usually 1/2 of the average value of the distance between all data points; the distance between the data points is calculated using the following formula:
the above formula is a distance calculation formula of two data points i and j in n dimensions, and x and y respectively represent the data of the data points i and j in each dimension.
2. The cluster number determining method comprises the following steps:
the clustering number K is an important index affecting the clustering effect, the clustering cannot be realized due to the fact that the clustering effect is too large, the data difference in the class is too large due to the fact that the clustering effect is too small, and at present, the value of K is manually determined by adopting experience judgment, so that the clustering result has larger uncertainty.
The method for determining the clustering number establishes two new parameters at two levels inside each cluster and between each cluster of the clustering result: data aggregation degree and center distance; the data aggregation is defined by the following formula:
where N is the number of data points, x j Data points, m i Is a cluster center.
The center distance is defined by the following formula:
d(m i ,m j ) Is cluster c i And c j Is the distance of the cluster center;
the two parameters D and C represent the similarity of data in the clusters and the dispersion degree among the clusters, so that a comprehensive evaluation index, namely a clustering effect index V (k), is formed;
V(k)＝(C-D)/(C+D)
the clustering effect index is an index for comprehensively evaluating the clustering effect aiming at a certain clustering number K, and the closer the clustering effect index is to 1, the better the clustering effect is. According to the method, iteration step length is set according to the data scale, and clustering effect indexes of all K values in a range are obtained, so that the optimal clustering number is selected.
3. The method for planning the shared bicycle electronic fence comprises the following steps:
in combination with the improved K-means algorithm, in order to find the proper position of the shared bicycle electronic fence, the distance between the shared bicycle parking demand points is utilized to cluster the shared bicycle electronic fence, the points with similar distances are marked into the same cluster, the central point position of the cluster is the electronic fence position, and the size of the cluster determines the fence size. The whole process can be divided into the following steps:
(1) According to the parking demand point data scale, the iteration range of the clustering number K is given, the iteration step length is adaptively selected in the range, and an iteration set of K is obtained;
(2) The improved K-means algorithm is adopted to gather the shared bicycle parking demand points into K classes, and data aggregation degree D and center distance C are calculated, so that clustering effect indexes V (K) with different K values are obtained;
(3) Selecting a K value with the clustering effect index closest to 1 as the number of the electronic fences;
(4) Calculating the distance between each parking demand point by adopting a data point circumference radiation model, and determining K initial clustering centers;
(5) For any parking demand point, solving the distance from the parking demand point to K clustering centers, and dividing the parking point into a cluster which is closest to the parking demand point;
(6) Updating the center point or the mass center position of each cluster by using a mean value and other methods;
(7) And (5) returning to the step (5) (6) after updating the K clustering centers, if the central point position change is smaller after iteration, considering that the stable state is reached, or reaching the set iteration times, and ending the iteration, namely outputting the positions of the K sharing bicycle electronic fences.
Step two: setting the capacity of the shared bicycle electronic fence: determining the actual capacity of each electronic fence according to the total number of vehicles, parking requirements and the actual available area size of the electronic fence of the shared bicycle;
step three: e-fence parking management and guiding:
(1) Selecting each electronic fence on the electronic map of the shared bicycle intelligent operation management service system according to the four-point coordinate frame of the actual parking area determined in the mode;
(2) Before riding, a user can inquire the number of the remaining space of the shared bicycle electronic fence near the destination through the mobile phone APP or the applet;
(3) When a user arrives at the electronic fence to prepare for parking the vehicle, the system judges whether the vehicle is in the range of the electronic fence according to the position data sent by the shared bicycle intelligent lock, and if the vehicle is judged to be in the electronic fence, and the number of the remaining space of the electronic fence is > =1, the user can smoothly lock the intelligent lock and finish parking the vehicle; if the vehicle is judged not to be located in the electronic fence, the user is not allowed to finish intelligent locking, and the user can inquire the nearest parked electronic fence at the mobile phone end APP or the applet in real time to finish sharing bicycle parking.
Based on the method, the embodiment of the invention also provides a sharing bicycle standard parking system based on the electronic fence, which comprises a mobile phone terminal APP or applet, a sharing bicycle intelligent lock and the sharing bicycle electronic fence; the mobile phone terminal APP or applet establishes data communication connection with the shared bicycle intelligent lock and the shared bicycle electronic fence respectively; the mobile phone terminal APP or the applet is used for displaying the position of the shared bicycle electronic fence in the area and the number of the shared bicycle in real time; and the sharing bicycle judges whether the bicycle is in the sharing bicycle electronic fence or not according to the position data sent by the intelligent lock.
To sum up: the invention provides a sharing bicycle standard parking method and system based on an electronic fence, which realize sharing bicycle electronic fence planning, electronic fence capacity setting and real-time monitoring based on an improved K-means clustering algorithm, and sharing bicycle parking standard management and guidance based on the electronic fence.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should be covered by the protection scope of the present invention by making equivalents and modifications to the technical solution and the inventive concept thereof.
Claims (6)
1. The sharing bicycle standard parking method based on the electronic fence is characterized by comprising the following steps of:
s1: sharing a bicycle electronic fence program: in combination with an improved K-means algorithm, in order to find a proper position of the shared bicycle electronic fence, the distance between the shared bicycle parking demand points is utilized to cluster the shared bicycle parking demand points, the points with similar distances are marked into the same cluster, the central point position of the cluster is the electronic fence position, and the size of the cluster determines the fence size;
s2: setting the capacity of the shared bicycle electronic fence: determining the actual capacity of each electronic fence according to the total number of vehicles, parking requirements and the actual available area size of the electronic fence of the shared bicycle;
s3: e-fence parking management and guiding: and the shared bicycle standard parking is realized by combining the shared bicycle intelligent operation management service system.
2. The method for parking a shared bicycle according to claim 1, wherein the method comprises the steps of: the improved K-means algorithm comprises an initial cluster center selection method and a cluster number determination method, wherein the initial cluster center selection method comprises the following steps:
according to the positions of the data points and the regional density, calculating the distance between the data points by adopting a data point circumference radiation model, obtaining a radiation data set of each data point, and selecting the most representative data point as a clustering center;
setting a threshold value P according to the data point scale, and allowing fine adjustment in the data execution process; defining r as the radius of the data point circular radiation model, and taking typically 1/2 of the average of all data point distances, the distance between the data points is calculated using the following formula:
the above formula is a distance calculation formula of two data points i and j in n dimensions, and x and y respectively represent the data of the data points i and j in each dimension.
3. The method for parking a shared bicycle according to claim 2, wherein the method comprises the steps of: the cluster number determining method comprises the following steps:
two new parameters are established at two levels inside each cluster and between each cluster of the clustering result: data aggregation degree and center distance, wherein the data aggregation degree is defined by the following formula:
in the above formula, N is the data point number, x j Data points, m i Is a clustering center;
the center distance is defined by the following formula:
d(m i ,m j ) Is cluster c i And c j Is the distance of the cluster center;
the two parameters D and C represent the similarity of data in the clusters and the dispersion degree among the clusters, so that a comprehensive evaluation index, namely a clustering effect index V (k), is formed;
V(k)＝(C-D)/(C+D)
the clustering effect index is an index for comprehensively evaluating the clustering effect aiming at a certain clustering number K, the closer the clustering effect index is to 1, the better the clustering effect is, the iteration step length is required to be set according to the data scale, the clustering effect indexes of all K values in the range are obtained, and therefore the optimal clustering number is selected.
4. A method for parking a shared bicycle in accordance with claim 3, wherein the method comprises the steps of: the specific steps in S1 are as follows:
s101: according to the parking demand point data scale, the iteration range of the clustering number K is given, the iteration step length is adaptively selected in the range, and an iteration set of K is obtained;
s102: the method comprises the steps that an improved K-means algorithm is adopted to gather the shared bicycle parking demand points into K types, and data aggregation degree D and center distance C are calculated, so that clustering effect indexes V (K) with different K values are obtained;
s103: selecting a K value with the clustering effect index closest to 1 as the number of the electronic fences;
s104: calculating the distance between each parking demand point by adopting a data point circumference radiation model, and determining K initial clustering centers;
s105: for any parking demand point, solving the distance from the parking demand point to K clustering centers, and dividing the parking point into a cluster which is closest to the parking demand point;
s106: updating the center point or the mass center position of each cluster by using a mean value method;
s107: and returning to S105 and S106 for the K updated clustering centers, if the central point position change is smaller after iteration, considering that the stable state is reached or the set iteration times are reached, and ending the iteration, namely outputting the positions of the K sharing bicycle electronic fences.
5. The method for parking a shared bicycle according to claim 1, wherein the specific method in S3 is as follows:
s301: selecting each electronic fence on the electronic map of the shared bicycle intelligent operation management service system according to the four-point coordinate frame of the determined actual parking area;
s302: before riding, a user inquires the number of remaining space of the shared bicycle electronic fence near the destination through a mobile phone APP or a small program;
s303: when a user arrives at the electronic fence to prepare for parking the vehicle, the system judges whether the vehicle is in the range of the electronic fence according to the position data sent by the shared bicycle intelligent lock;
s304: if the vehicle is judged to be in the electronic fence and the number of remaining empty bits of the electronic fence is > =1, the user can smoothly lock the intelligent lock and finish vehicle parking; if the vehicle is judged not to be located in the electronic fence, the user is not allowed to finish intelligent locking, and the user can inquire the nearest parked electronic fence at the mobile phone end APP or the applet in real time to finish sharing bicycle parking.
6. The sharing bicycle standard parking system based on the electronic fence is characterized by comprising a mobile phone terminal APP or applet, a sharing bicycle intelligent lock and a sharing bicycle electronic fence; the mobile phone terminal APP or applet establishes data communication connection with the shared bicycle intelligent lock and the shared bicycle electronic fence respectively; the mobile phone terminal APP or the applet is used for displaying the position of the shared bicycle electronic fence in the area and the number of the shared bicycle in real time; and the sharing bicycle judges whether the bicycle is in the sharing bicycle electronic fence or not according to the position data sent by the intelligent lock.
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| CN117853714A (en) * | 2024-03-06 | 2024-04-09 | 北京阿帕科蓝科技有限公司 | Parking area generation method, device, computer equipment and storage medium |
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| CN117853714A (en) * | 2024-03-06 | 2024-04-09 | 北京阿帕科蓝科技有限公司 | Parking area generation method, device, computer equipment and storage medium |
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