A periodic deployment method for shared bicycles based on crowd intelligence perception

Abstract

The present invention relates to a method for periodic placement of shared bicycles based on crowd intelligence perception. After a user submits a placement opinion of a shared bicycle, the platform and the user jointly select a placement area for the shared bicycle. At the same time, the platform checks the confidence R of the collected perception data, and transfers a portion of the shared bicycles whose placement locations exceed the user's usage range to areas with higher usage intentions. Finally, the platform encourages the generation of more positive user opinions by rewarding users who participate in the periodic placement opinions of shared bicycles. The present invention can periodically and reasonably place shared bicycles, solve the shortcomings of long bicycle placement time in remote areas, long manual placement time, difficult operation, and low rationality. Based on crowd intelligence perception technology, it realizes the placement of shared bicycles according to people's wishes, meets people's travel needs; makes full use of shared bicycles, facilitates travel, and can be widely used in areas where shared bicycles are used to improve the utilization rate of shared bicycles.

Description

Technical Field

[0001] The present invention relates to a data processing system or method specifically suitable for administrative, commercial, financial, management, supervisory or forecasting purposes; the technical field of processing systems or methods specifically suitable for administrative, commercial, financial, management, supervisory or forecasting purposes not included in other categories, and particularly to a method for periodic deployment of shared bicycles based on crowd intelligence perception. Background Art

[0002] In recent years, with the development of cities, it is inevitable that urban congestion has become more and more serious, especially in large and medium-sized cities.

[0003] Due to the need for travel, shared bicycles came into being. On the one hand, shared bicycles alleviate traffic congestion and facilitate the "last mile" of travel. On the other hand, they reduce the use of cars to a certain extent, making people's travel more low-carbon and environmentally friendly, and reducing air pollution.

[0004] However, while meeting people's needs for safe, fast and comfortable travel, the deployment of shared bicycles has also attracted people's attention. Many bicycles are in remote locations for a long time without being managed and dispatched, which constitutes an invisible waste of resources. At the same time, there are also users in some areas who want to use shared bicycles, but there are no shared bicycles around or the number of shared bicycles is too small to meet the demand.

[0005] In order to better meet people's travel needs, a reasonable method of deploying shared bicycles is needed. Summary of the invention

[0006] The present invention solves the problems existing in the prior art and provides an optimized method for periodic deployment of shared bicycles based on crowd intelligence perception, which can transfer some shared bicycles with more remote deployment locations to areas with higher willingness to use the bicycles and better match the actual needs of users.

[0007] The technical solution adopted by the present invention is a method for periodically placing shared bicycles based on crowd intelligence perception, and the method comprises the following steps:

[0008] Step 1: Users submit opinions on the deployment of shared bicycles;

[0009] Step 2: The platform and the user jointly select the shared bicycle deployment area as the perception data;

[0010] Step 3: The platform checks the confidence R of the collected perception data;

[0011] Step 4: The platform will transfer some shared bicycles that are placed beyond the user's usage range to areas with higher user willingness;

[0012] Step 5: The platform rewards users who participate in the periodic deployment of shared bicycles.

[0013] Preferably, in step 1, the opinions on the placement of shared bicycles include transferring shared bicycles and directly placing shared bicycles.

[0014] Preferably, step 2 comprises the following steps:

[0015] Step 2.1: Obtain the user's current location or the location P where the user manually enters the delivery intention;

[0016] Step 2.2: The platform calibrates several planned areas A based on the position P in step 2.1;

[0017] Step 2.3: If the user agrees with one or more of the planned areas, just click to confirm. Otherwise, the user manually enters the location or area where they want to deploy the shared bicycles.

[0018] Preferably, the step 2.2 comprises the following steps:

[0019] Step 2.2.1: The platform queries historical records and obtains several areas A' with historical delivery intentions but unsuccessful delivery;

[0020] Step 2.2.2: Screen these areas A';

[0021] Step 2.2.3: Calculate the distance between the selected area and the location in step 2.1, and sort them from near to far according to the distance;

[0022] Step 2.2.4: If the number of filtered regions is greater than or equal to N, take the first N regions and output them in order as planned region A. Otherwise, proceed to the next step;

[0023] Step 2.2.5: The order of the screened areas remains unchanged, and the areas recommended by the platform are added to the screened areas as planned area A. The total number of planned area A is N.

[0024] Preferably, step 3 comprises the following steps:

[0025] Step 3.1: Based on the opinions on the placement of shared bicycles, if the shared bicycles are transferred, the placement confidence W1 of the shared bicycles in the current transfer-out area is calculated; if the shared bicycles are directly placed, the placement confidence W2 of the shared bicycles in the current transfer-in area is calculated;

[0026] Step 3.2: Based on the opinions on the placement of shared bicycles, if the shared bicycles are transferred, the remoteness confidence D1 of the shared bicycles in the current transfer-out area is calculated; if the shared bicycles are directly placed, the remoteness confidence D2 of the shared bicycles in the current transfer-in area is calculated;

[0027] Step 3.3: Based on the opinions on the deployment of shared bicycles,

[0028] If it is a shared bicycle transfer, the total confidence R=W1*D1;

[0029] If shared bicycles are deployed directly, the total confidence R=W2*D2.

[0030] Preferably, in step 3.1, when transferring a shared bicycle, the number of shared bicycles in the current transfer-out area is obtained. If the number of shared bicycles is 0, W1=0, otherwise, W1=1;

[0031] When shared bicycles are directly deployed, the number of shared bicycles in the current transfer area is obtained. If the number of shared bicycles is 0, W2=1. If the number of shared bicycles is not 0, W2 is set based on the per capita ownership rate of shared bicycles in the current transfer area. The higher the per capita ownership rate, the smaller W2. When the per capita ownership rate exceeds the threshold, W2=0.

[0032] Preferably, the step 3.2 comprises the following steps:

[0033] Step 3.2.1: Obtain the central longitude and latitude of a city, and obtain a circular area with the central longitude and latitude and a preset radius;

[0034] Step 3.2.2: When transferring a shared bicycle, if the shared bicycle currently transferred out of the area is within the circular area, then D1=0, otherwise D1=3, and proceed to step 3.3;

[0035] If the shared bicycles are deployed directly, proceed to the next step;

[0036] Step 3.2.3: If the location reported by the user is within the circular area, it is not a remote area; otherwise, it is a remote area determined by the platform;

[0037] Step 3.2.4: Obtain historical data, count the number of times the location reported by the user is submitted as remote, and calculate the user bias rate n, n=P / Q, where P is the number of times the location was reported as remote to the platform in history, and Q is the total number of valid location information reported by the current city;

[0038] Step 3.2.5: If n is less than the threshold, then this location is not a remote area, otherwise, it is a remote area judged by the user;

[0039] Step 3.2.6: If it is a remote area determined by the user and the platform, D2=1;

[0040] If the user or platform determines that it is a remote area, D2=2;

[0041] Otherwise D2=3;

[0042] Proceed to step 3.3.

[0043] Preferably, in step 4, when transferring shared bicycles in remote locations to areas with higher willingness to use the bicycles, the transfer ratio is 50%.

[0044] Preferably, in step 5, the platform rewards participants according to the authenticity R of the perception data uploaded by the user, whether the area Y where the bicycle is to be deployed is recommended, the number M of recommended areas, the remoteness D of the recommended areas, and the time points T spent on completing the perception task.

[0045] The reward is based on the crowd-sensing incentive function; the crowd-sensing incentive function H=α*C+β*Y+γ*M, where α, β and γ are weight coefficients, α+β+γ=1, C is reliability, C=T*R*D.

[0046] The present invention relates to an optimized shared bicycle periodic deployment method based on crowd intelligence perception. After a user submits a shared bicycle deployment opinion, the platform and the user jointly select a shared bicycle deployment area. At the same time, the platform checks the confidence R of the collected perception data, and transfers a portion of shared bicycles whose deployment locations exceed the user's usage range to areas with higher usage intentions. Finally, the platform encourages the generation of more positive user opinions by rewarding users who participate in the shared bicycle periodic deployment opinions.

[0047] The present invention can reasonably deploy shared bicycles periodically, solving the shortcomings of long bicycle placement time in remote areas, long manual deployment time, difficult operation, and low rationality. Based on crowd intelligence perception technology, it realizes the deployment of shared bicycles according to people's wishes and meets people's travel needs; it makes full use of shared bicycles and facilitates travel. It can be widely used in areas where shared bicycles are used to improve the utilization rate of shared bicycles. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0049] The present invention is further described in detail below in conjunction with embodiments, but the protection scope of the present invention is not limited thereto.

[0050] The present invention relates to a method for periodic deployment of shared bicycles based on crowd-sensing. Crowd-sensing is a new data acquisition mode that combines the idea of ​​crowdsourcing with the perception capability of mobile devices. It refers to forming an interactive and participatory perception network through people's existing mobile devices, and issuing perception tasks to individuals or groups in the network to complete, thereby helping professionals or the public to collect data, analyze information and share knowledge. The method comprises the following steps.

[0051] Step 1: Users submit their opinions on the deployment of shared bicycles.

[0052] In step 1, the opinions on the deployment of shared bicycles include transferring shared bicycles and directly deploying shared bicycles.

[0053] In the present invention, transferring a shared bicycle means that the user uses mobile phone positioning or manually inputs the address of the shared bicycle, and reports the location information of the shared bicycle that is placed in a more remote position to the platform, which is generally a transfer from a remote area to a non-remote area; and directly placing a shared bicycle means that the user only wants to report the area where the shared bicycle needs to be placed, so only locates the location or manually inputs the location and reports it to the platform; this is an intervention-type demand, and the other is a spontaneous demand.

[0054] Step 2: The platform and users jointly select the shared bicycle deployment area as perception data.

[0055] The step 2 comprises the following steps:

[0056] Step 2.1: Obtain the user's current location or the location P where the user manually enters the delivery intention;

[0057] Step 2.2: The platform calibrates several planned areas A based on the position P in step 2.1;

[0058] The step 2.2 comprises the following steps:

[0059] Step 2.2.1: The platform queries historical records and obtains several areas A' with historical delivery intentions but unsuccessful delivery;

[0060] Step 2.2.2: Screen these areas A';

[0061] Step 2.2.3: Calculate the distance between the selected area and the location in step 2.1, and sort them from near to far according to the distance;

[0062] Step 2.2.4: If the number of filtered regions is greater than or equal to N, take the first N regions and output them in order as planned region A. Otherwise, proceed to the next step;

[0063] Step 2.2.5: The order of the screened areas remains unchanged, and the areas recommended by the platform are added to the screened areas as planned area A. The total number of planned area A is N.

[0064] Step 2.3: If the user agrees with one or more of the planned areas, just click to confirm. Otherwise, the user manually enters the location or area where they want to deploy the shared bicycles.

[0065] In the present invention, the platform will recommend several areas based on the location information uploaded by the user, for the user to choose the area where he wants to deploy the shared bicycle; if the user is not satisfied with the area recommended by the platform, the user will manually input the location area where he wants to deploy the shared bicycle.

[0066] In the present invention, before calibrating several planned areas, a round of screening can be performed first, and the main screening criteria are distance and whether it is an already placed area. For example, a circle is made with the location information as the center as the intended area, and the radius of the circle is preset, such as 500-1000m. If the intended area overlaps or partially overlaps with the existing placement location, the overlapping area is removed. This is the first step of screening; further, several planned areas can be calibrated according to the marked buildings in the processed intended area, and the density of the planned area gradually decreases from the marked buildings to the unmarked area.

[0067] In the present invention, platform recommendation means that the platform gives priority to selecting the top areas where users wanted to deploy shared bicycles in the past or popular areas in the city for users to choose; of course, substitute areas can also be obtained by relaxing the radius of the intended area.

[0068] Step 3: The platform checks the confidence R of the collected perception data.

[0069] The step 3 comprises the following steps:

[0070] Step 3.1: Based on the opinions on the placement of shared bicycles, if the shared bicycles are transferred, the placement confidence W1 of the shared bicycles in the current transfer-out area is calculated; if the shared bicycles are directly placed, the placement confidence W2 of the shared bicycles in the current transfer-in area is calculated;

[0071] In step 3.1, when transferring a shared bicycle, the number of shared bicycles in the current transfer-out area is obtained. If the number of shared bicycles is 0, W1=0, otherwise, W1=1;

[0072] When shared bicycles are directly deployed, the number of shared bicycles in the current transfer area is obtained. If the number of shared bicycles is 0, W2=1. If the number of shared bicycles is not 0, W2 is set based on the per capita ownership rate of shared bicycles in the current transfer area. The higher the per capita ownership rate, the smaller W2. When the per capita ownership rate exceeds the threshold, W2=0.

[0073] Step 3.2: Based on the opinions on the placement of shared bicycles, if the shared bicycles are transferred, the remoteness confidence D1 of the shared bicycles in the current transfer-out area is calculated; if the shared bicycles are directly placed, the remoteness confidence D2 of the shared bicycles in the current transfer-in area is calculated;

[0074] The step 3.2 comprises the following steps:

[0075] Step 3.2.1: Obtain the central longitude and latitude of a city, and obtain a circular area with the central longitude and latitude and a preset radius;

[0076] Step 3.2.2: When transferring a shared bicycle, if the shared bicycle currently transferred out of the area is within the circular area, then D1=0, otherwise D1=3, and proceed to step 3.3;

[0077] If the shared bicycles are deployed directly, proceed to the next step;

[0078] Step 3.2.3: If the location reported by the user is within the circular area, it is not a remote area; otherwise, it is a remote area determined by the platform;

[0079] Step 3.2.4: Obtain historical data, count the number of times the location reported by the user is submitted as remote, and calculate the user bias rate n, n=P / Q, where P is the number of times the location was reported as remote to the platform in history, and Q is the total number of valid location information reported by the current city;

[0080] Step 3.2.5: If n is less than the threshold, then this location is not a remote area, otherwise, it is a remote area judged by the user;

[0081] Step 3.2.6: If it is a remote area determined by the user and the platform, D2=1;

[0082] If the user or platform determines that it is a remote area, D2=2;

[0083] Otherwise D2=3;

[0084] Proceed to step 3.3.

[0085] Step 3.3: Based on the opinions on the deployment of shared bicycles,

[0086] If it is a shared bicycle transfer, the total confidence R=W1*D1;

[0087] If shared bicycles are deployed directly, the total confidence R=W2*D2.

[0088] In the present invention, after collecting the perception data, it is necessary to check the validity (confidence) of the perception data. Based on the placement opinions of the shared bicycles selected by the user, it is checked whether there are bicycles in the location area reported by the user. This serves as the basis for credibility and feasibility. Under different placement opinions, there are differences in the judgment of validity.

[0089] In the present invention, the actual deployment first includes determining the actual number of shared bicycles:

[0090] When transferring a shared bicycle, if the number of shared bicycles in the current transfer area is 0, no shared bicycles can be transferred out regardless of its remoteness, so the shared bicycle placement confidence W1 is 0, otherwise it is 1;

[0091] When sharing bicycles are directly deployed, the number of shared bicycles in the current transfer area must be obtained first. When there are no shared bicycles, it is assumed that there is a demand for deployment, and W2 is 1. If there are shared bicycles, W2 must be configured based on the per capita ownership rate of shared bicycles in the current transfer area. Within the threshold range, the higher the per capita ownership rate, the smaller W2. When the per capita ownership rate exceeds the threshold, it indicates that there is an actual surplus of shared bicycles, and W2 is 0. The per capita ownership rate threshold here can be set and adjusted based on the survey of researchers.

[0092] In the present invention, the actual deployment also needs to determine the remoteness of the shared bicycles:

[0093] When transferring a shared bicycle, the remoteness is assigned in the area marked as remote. If it is in the area, it is not actually remote and D1 is 0. Otherwise, D1 is set to 3.

[0094] When sharing bicycles are directly deployed, the platform determines whether the area is within the designated area, and the user bias n is used as the user's standard for judging remote areas. Combined with the judgment of both parties, if the area is judged as a remote area by both the user and the platform, it is indeed a remote area, D2=1. If the user or the platform determines it to be a remote area, there is a cognitive difference in remote areas, D2=2. If neither party actually determines it to be a remote area, then since it is in a non-remote area, it should be covered and there should be no separate demand for deployment. Therefore, D2=3 expresses a very high confidence level and it should be covered as soon as possible.

[0095] In the present invention, there are several situations:

[0096] (1) The user’s request is to transfer a shared bicycle, but there is no shared bicycle to be transferred, and R is 0;

[0097] (2) The user’s demand is to transfer shared bicycles, and there are shared bicycles that can be transferred;

[0098] (2-1) However, if the area is actually a non-remote area and should not be transferred out, then R is 0;

[0099] (2-2) The area is a remote area and should be transferred out, and R is 3;

[0100] (3) The user's demand is to directly deploy shared bicycles, but there are currently no shared bicycles in the transfer area;

[0101] (3-1) The area is a remote area and can be deployed, R=1;

[0102] (3-2) The area may be a remote area and should be deployed, R=2;

[0103] (3-3) The area is not actually a remote area and should be deployed, R=3;

[0104] (4) The user's demand is to directly deploy shared bicycles. There are currently shared bicycles in the transfer area and the per capita ownership rate does not exceed the threshold;

[0105] (4-1) The area is a remote area and can be deployed, R=W2; 0<W2<1;

[0106] (4-2) The area may be a remote area and should be deployed, R=2W2; 0<W2<1;

[0107] (4-3) The area is not actually a remote area, so it should be deployed, R=3W2; 0<W2<1;

[0108] (5) If the user’s demand is to directly deploy shared bicycles and there are shared bicycles in the current transfer area and the per capita ownership rate exceeds the threshold (too high), then R = 0.

[0109] In the present invention, the confidence R is used as a standard for whether to confirm the opinion on the placement of shared bicycles. If it is 0, it is negative, and if it is 1, it is positive.

[0110] Step 4: The platform will transfer some shared bicycles that are placed in locations beyond the user's usage range to areas with higher user willingness.

[0111] In the present invention, a higher willingness refers to a region with a historical intention to deploy but has not been successfully deployed, and the historical deployment intention amount is larger than other regions; under the technical conditions of the present invention, shared bicycles in the region where the opinions on transferring shared bicycles are proposed can be preferentially transferred to the region where the opinions on directly deploying shared bicycles are proposed.

[0112] In the present invention, step 4 is a periodic operation.

[0113] Step 5: The platform rewards users who participate in the periodic deployment of shared bicycles.

[0114] In step 5, the platform rewards participants according to the authenticity R of the perception data uploaded by the user, whether the area Y where the bicycle is to be deployed is recommended, the number M of recommended areas, the remoteness D of the recommended areas, and the time points T spent on completing the perception task.

[0115] The reward is based on the crowd-sensing incentive function; the crowd-sensing incentive function H=α*C+β*Y+γ*M, where α, β and γ are weight coefficients, α+β+γ=1, C is reliability, C=T*R*D.

[0116] In the present invention, if the participant completes the perception task within the specified time, T=2, otherwise T=1.

[0117] In the present invention, in step 5, points can be issued according to the group intelligence perception incentive function value, and after a periodic time, the points obtained by each participant are accumulated and counted, and participants who reach a certain score can be rewarded with bicycle riding coupons or cash red envelopes.

[0118] In the present invention, step 5 is a periodic operation.

[0119] In the present invention, after users submit their opinions on the placement of shared bicycles, the platform and users jointly select the placement areas of shared bicycles. At the same time, the platform checks the confidence R of the collected perception data and transfers some shared bicycles whose placement locations are beyond the user's usage range to areas with higher usage intentions. Finally, the platform encourages the generation of more positive user opinions by rewarding users who participate in the periodic placement of shared bicycles.

[0120] The present invention can reasonably deploy shared bicycles periodically, solving the shortcomings of long bicycle placement time in remote areas, long manual deployment time, difficult operation, and low rationality. Based on crowd intelligence perception technology, it realizes the deployment of shared bicycles according to people's wishes and meets people's travel needs; it makes full use of shared bicycles and facilitates travel. It can be widely used in areas where shared bicycles are used to improve the utilization rate of shared bicycles.

Claims

1. A method for periodic deployment of shared bicycles based on crowd intelligence perception, characterized in that: The method comprises the following steps: Step 1: Users submit opinions on the deployment of shared bicycles; Step 2: The platform and the user jointly select the shared bicycle deployment area as the perception data, including the following steps: Step 2.1: Obtain the user's current location or the location P where the user manually enters the delivery intention; Step 2.2: The platform calibrates several planned areas A based on the position P in step 2.1; Step 2.3: If the user agrees with one or more of the planned areas, he / she can directly click to confirm. Otherwise, the user manually enters the location or area where he / she wants to deploy the shared bicycles. Step 3: The platform checks the confidence R of the collected perception data, including the following steps: Step 3.1: Based on the opinions on the placement of shared bicycles, if it is a transfer of shared bicycles, the number of shared bicycles in the current transfer-out area is obtained. If the number of shared bicycles is 0, the placement confidence of shared bicycles in the current transfer-out area is W1=0, otherwise, W1=1; if it is a direct placement of shared bicycles, the number of shared bicycles in the current transfer-in area is obtained. If the number of shared bicycles is 0, the placement confidence of shared bicycles in the current transfer-in area is W2=1. If the number of shared bicycles is not 0, W2 is set based on the per capita ownership rate of shared bicycles in the current transfer-in area. The higher the per capita ownership rate, the smaller W2. When the per capita ownership rate exceeds the threshold, W2=0; Step 3.2: Based on the opinions on the deployment of shared bicycles, the following steps are included: Step 3.2.1: Obtain the central longitude and latitude of a city, and obtain a circular area with the central longitude and latitude and a preset radius; Step 3.2.2: When transferring a shared bicycle, if the shared bicycle in the current transfer-out area is within the circular area, the remoteness confidence of the shared bicycle in the current transfer-out area is D1=0, otherwise D1=3, and step 3.3 is performed; If the shared bicycles are deployed directly, proceed to the next step; Step 3.2.3: If the location reported by the user is within the circular area, it is not a remote area; otherwise, it is a remote area determined by the platform; Step 3.2.4: Obtain historical data, count the number of times the location reported by the user is submitted as remote, and calculate the user bias rate n, n=P / Q, where P is the number of times the location was reported as remote to the platform in history, and Q is the total number of valid location information reported by the current city; Step 3.2.5: If n is less than the threshold, then this location is not a remote area, otherwise, it is a remote area judged by the user; Step 3.2.6: If it is a remote area determined by the user and the platform, the remoteness confidence of the shared bicycle currently transferred to the area is D2 = 1; If the user or platform determines that it is a remote area, D2=2; Otherwise D2=3; Proceed to step 3.3; Step 3.3: Based on the opinions on the deployment of shared bicycles, If it is a shared bicycle transfer, the total confidence R=W1*D1; If shared bicycles are directly deployed, the total confidence R=W2*D2; Step 4: The platform will transfer some shared bicycles that are placed beyond the user's usage range to areas with higher user willingness; Step 5: The platform rewards users who participate in the periodic deployment of shared bicycles. The platform rewards participants according to the authenticity R of the perception data uploaded by the user, whether the area Y where the bicycles are to be deployed is recommended, the number of recommended areas M, the remoteness D of the recommended areas, and the time T spent on completing the perception task. The reward is based on the crowd intelligence perception incentive function; the crowd intelligence perception incentive function H=α*C+β*Y+γ*M, where α, β and γ are weight coefficients, α+β+γ=1, C is reliability, C=T*R*D, and if the participant completes the perception task within the specified time, T=2, otherwise T=1.

2. According to claim 1, a method for periodic deployment of shared bicycles based on crowd intelligence perception is characterized by: In step 1, the opinions on the deployment of shared bicycles include transferring shared bicycles and directly deploying shared bicycles.

3. According to claim 1, a method for periodic deployment of shared bicycles based on crowd intelligence perception is characterized in that: The step 2.2 comprises the following steps: Step 2.2.1: The platform queries historical records and obtains several areas A' with historical delivery intentions but unsuccessful delivery; Step 2.2.2: Screen these areas A'; Step 2.2.3: Calculate the distance between the selected area and the location in step 2.1, and sort them from near to far according to the distance; Step 2.2.4: If the number of filtered regions is greater than or equal to N, take the first N regions and output them in order as planned region A. Otherwise, proceed to the next step; Step 2.2.5: The order of the screened areas remains unchanged, and the areas recommended by the platform are added to the screened areas as planned area A. The total number of planned area A is N.

4. The method for periodically placing shared bicycles based on crowd intelligence perception according to claim 1 is characterized in that: In step 4, when shared bicycles in remote locations are transferred to areas with higher willingness to use the bicycles, the transfer ratio is 50%.

Images