CN116137109A - Mechanical parking equipment control system and method based on cloud computing - Google Patents
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Abstract
The invention relates to the technical field of equipment control, in particular to a mechanical parking equipment control system and method based on cloud computing, comprising the following steps: collecting user information, forming a user set, collecting video data of a target user in a certain time sequence, and intercepting video images at fixed time to form an image set; collecting the positions of the vehicle taking openings to form a vehicle opening position set; acquiring all acquired data and storing the acquired data; analyzing the walking speed of the target user, predicting the position of the target user at the next time point, and analyzing the time length of the target user reaching the position of each vehicle taking port; matching the positions of vehicles, analyzing the time length from the operation of the target vehicle to the positions of the vehicle taking openings, and selecting the optimal vehicle taking opening; confirming the optimal vehicle taking port positions of all users in the user set, judging whether the positions overlap, and performing real-time control; displaying by using a mobile phone terminal; the method is beneficial to the selection of a vehicle taking port and the use feeling of mechanical equipment for a user, and the time for taking the vehicle by the user is greatly reduced.
Description
Technical Field
The invention relates to the technical field of equipment control, in particular to a mechanical parking equipment control system and method based on cloud computing.
Background
The mechanical parking equipment is a general name for transporting and parking automobile equipment in a mechanical garage, and along with the development of social economy, the living standard of people is improved, and automobiles gradually enter ordinary families. The problems of increasingly-increased automobiles, increasingly-blocked traffic and difficult parking in various large and medium cities are gradually revealed, so that people pay more attention to the traffic conditions of the cities, and huge business opportunities and wide markets are brought to the mechanical parking equipment industry.
Although mechanical parking can improve land utilization and space utilization and is convenient to operate, the mechanical parking has some disadvantages: because the running speed of the mechanical equipment is low, a user needs to spend a certain time in the process of taking the car, and meanwhile, as the number of parked cars is increased, the time for taking the car by the user is also increased, so that the use feeling of the mechanical equipment by the user is seriously influenced, and the development and popularization of the mechanical equipment are not facilitated.
Disclosure of Invention
The invention aims to provide a mechanical parking equipment control system and method based on cloud computing, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a mechanical parking equipment control method based on cloud computing comprises the following steps:
step S100: collecting all user information entering a garage by using a camera, confirming and marking the vehicle taking behaviors of the user to form a user set, setting any user in the user set as a target user, collecting video data of the target user under a certain time sequence based on the target user, and intercepting video images at regular time by using a time round algorithm to form an image set; collecting all the vehicle taking opening positions in the mechanical parking garage to form a vehicle opening position set; meanwhile, the operation speed of the mechanical parking equipment is collected;
step S200: acquiring all acquired data and storing the acquired data;
step S300: analyzing the walking speed and the real-time position of the target user according to the image set, predicting the position of the target user at the next time point based on the walking speed and the real-time position, and setting the predicted position point; based on the predicted position points and the vehicle mouth position sets, analyzing the time length of the target user reaching each vehicle mouth position to form a user time length set; according to the matching vehicle information and the vehicle position of a target user, setting the vehicle as a target vehicle, analyzing the time length of the target vehicle running to each vehicle taking position based on the target vehicle position and the vehicle opening position set, forming a vehicle time length set, and further selecting an optimal vehicle taking opening according to the user time length set and the vehicle time length set;
step S400: confirming the optimal vehicle taking port positions of all users in the user set, judging whether the positions overlap, and if the positions do not overlap, respectively controlling all target vehicles to reach the optimal vehicle taking port positions in real time; if overlapping occurs, extracting overlapping user information, intelligently distributing the vehicle taking ports of the overlapping users according to the time difference of the overlapping users reaching the vehicle taking port positions, and controlling the target vehicles to reach the vehicle taking port positions in real time;
step S500: and displaying and reminding the route of the user to the position of each vehicle taking port by using the mobile phone terminal.
Further, step S100 includes:
s110: the method comprises the steps that a camera is used for carrying out matching screening on all collected user information entering a garage and user information of a vehicle cloud platform, and if matching is successful, user vehicle taking behaviors are confirmed and marked to form a user set A;
s120: setting any user in the user set A as a target user, acquiring video data of the target user under a certain time sequence based on the target user, and intercepting the video image every tms at regular time by using a time round algorithm to form an image set B= { B1, B2, …, bn }, wherein B1, B2, …, bn represent the image data of the target user at the 1 st, 2 nd, … th and n th time points; the time round algorithm belongs to a conventional technical means of a person skilled in the art, so that excessive details are not made in the application;
s130: collecting all vehicle taking opening positions in the mechanical parking garage to form a vehicle opening position set C= { (x 1, y 1), (x 2, y 2), …, (xm, ym) }, wherein (x 1, y 1), (x 2, y 2), …, (xm, ym) represents the positions of vehicle taking openings 1,2, … and m; and meanwhile, the operation speed of the mechanical parking equipment is v.
Further, step S200 includes: and acquiring all acquired data by utilizing a database and storing the acquired data.
Further, step S300 includes:
s310: acquiring an image set B, and performing pixel fusion on n image data by using a coincidence algorithm to form new image data bn+1; based on the image data bn+1 and the image set B, comparing pixel characteristics of any image data bz and image data bn+1 in the image set B, screening different pixels generated in the image data bn+1, constructing a two-dimensional plane coordinate system according to the screened pixels, and forming a user position set B' = { (p 1, q 1), (p 2, q 2), …, (pn, qn) }, wherein, (p 1, q 1), (p 2, q 2), …, (pn, qn) represents pixel positions of the 1 st, 2 nd, … th and n th time point target users; according to the distance di= v [ (pi+1-pi) from (pi+1, qi+1) to any position (pi, qi) in the user position set B 2 +(qi+1-qi) 2 ]Wherein i=1, 2, …, n-1, and time t gives the walking speed of the target user v= (d1+d2+ … +dn)/nt; the coincidence algorithm can carry out pixel fusion on the image textures with the same pixel points, and is beneficial to image analysis;
s320: and (3) performing straight line fitting based on the user position set B' to obtain a fitting straight line equation: qi=fb+ (hb) pi, where hb, fb are the slope and intercept, respectively, after straight line fitting; based on the fitting linear equation qi, predicting the position of the target user at the n+1th time point, and setting the position as a predicted position point (pn+1, qn+1); distance h between arbitrary vehicle mouth position (xf, yf) and predicted position point (pn+1, qn+1) according to vehicle mouth position set C 1 f=√[(pn+1-xf) 2 +(qn+1-yf) 2 ]Obtain the user distance set h1= { H 1 1,h 1 2,…,h 1 m }, wherein h 1 1,h 1 2,…,h 1 m represents the distance from the target user to the 1 st, 2 nd, … th and m th vehicle taking openings, and according to any distance H in the user distance set H1 1 f and walking speed v to obtain user time length t 1 f=h 1 f/v, further obtaining a user duration set: t1= { T 1 1,t 1 2,…,t 1 m }, where t 1 1,t 1 2,…,t 1 m represents the duration from the target user to the 1 st, 2 nd, … th and m pick-up openings;
s330: the vehicle cloud platform is used for matching user vehicle information according to a target user, setting the user vehicle information as a target vehicle, obtaining the position of the target vehicle as (C, g) by using a positioning algorithm, and respectively obtaining the distance h from the target vehicle to each vehicle taking position based on the target vehicle position and a vehicle opening position set C 2 f=√[(c-xf) 2 +(g-yf) 2 ]Form a vehicle distance set h2= { H 2 1,h 2 2,…,h 2 m }, wherein h 2 1,h 2 2,…,h 2 m represents the distance from the target vehicle to the 1 st, 2 nd, … th and m th vehicle taking openings; based on the vehicle distance set H2 and the operating speed v of the mechanical parking device, a vehicle duration set is obtained: t2= { T 2 1,t 2 2,…,t 2 m }, where t 2 1,t 2 2,…,t 2 m represents the duration from the target vehicle to the 1 st, 2 nd, … th and m vehicle taking ports;
s340: acquiring a user duration set T1 and a vehicle duration set T2, and calculating a vehicle mouth loss score index kf= (T) based on any f-th vehicle taking mouth 1 f+t 2 f) 2 -(t 1 f-t 2 f) 2 Traversing the duration sets T1 and T2 to obtain a score set K= { K1, K2 … and km }, wherein K1, K2 and K … are loss scores from a target user to 1 st, 2 nd, … th and m th vehicle openings, and based on the score set K, carrying out ascending processing on the loss score index kf by using an bubbling sequencing algorithm, wherein the preference level of the corresponding vehicle opening to the target user is gradually reduced, and if the loss score is lower, the probability that the target user selects the vehicle opening is higher and the time for the user to arrive at the corresponding vehicle opening is shorter, so that the vehicle opening is more convenient for the user; based on the preferred level, the vehicle is takenThe port locations are ordered to obtain a vehicle port set C = { (x 1', y 1'), (x 2', y 2'), …, (xm ', ym') } wherein (x 1', y 1'), (x 2', y 2') …, (xm ', ym') represents the 1 st, 2 nd, … th, m vehicle pick-up port locations with progressively lower target user preference levels.
Further, step S400 includes:
s410: determining the optimal vehicle taking position (x 1', y 1') of the target user, traversing the user set A, respectively obtaining the optimal vehicle taking positions of all users according to the step S300 to form an optimal vehicle taking position set E, simultaneously comparing the vehicle taking position set C with the optimal vehicle taking position set E, and screening the non-optimal vehicle taking positions to form a non-optimal vehicle taking position set W; traversing the optimal vehicle taking port position set E, judging whether repeated optimal vehicle taking port positions exist or not, and if the repeated vehicle taking port positions do not exist, respectively controlling target vehicles of all users to reach the optimal vehicle taking port positions in real time;
s420: if the repeated optimal vehicle taking port positions exist, extracting the optimal vehicle taking port positions (xr ', yr') which are arbitrarily repeated, further extracting beta pieces of user information which are repeatedly generated, respectively acquiring time points when any user arrives at a predicted position point and time periods when the any user arrives at the (xr ', yr') based on the beta pieces of user information, obtaining time points when the any user arrives at the optimal vehicle taking port positions (xr ', yr'), and forming a time point set S= { S1, S2, …, S beta }, wherein S1, S2, … and S beta represent time points when the 1 st, 2 nd, … th and beta users which are repeatedly generated arrive at the optimal vehicle taking port positions; and according to the score set K and the non-optimal vehicle opening set W of any user, the inferior vehicle opening positions of the 2 nd, 3 rd, … th and beta th users are distributed intelligently in sequence, and target vehicles of all users are controlled to reach the corresponding vehicle opening positions in real time.
Further, step S500 includes: and displaying and reminding the route of the user reaching each vehicle taking opening position by using the mobile phone terminal, and monitoring in real time until the user successfully takes the vehicle.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, the time length of the user reaching the vehicle taking port is compared with the time length of the vehicle reaching the vehicle taking port, the time length loss of the vehicle reaching the vehicle taking port is judged by using the vehicle port loss score index, the priority level of each vehicle taking port to the user is confirmed according to the time length loss, the vehicle taking port positions are further ordered according to the priority level, the selection of the user on the vehicle taking port is facilitated, the time of the user for taking the vehicle is greatly reduced, and the use feeling of the user on mechanical equipment is improved; whether the optimal vehicle taking port positions are repeated or not is judged for all users entering the garage, and according to different results, intelligent control is carried out on the selection of the vehicles reaching each vehicle taking port position, so that the intelligence of the system is improved, the vehicle taking time length is shortened due to the fact that the users wait for a long time, and convenience for the users is facilitated.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a mechanical parking device control system based on cloud computing in accordance with the present invention;
fig. 2 is a flowchart of a method for controlling a mechanical parking device based on cloud computing 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.
Referring to fig. 1-2, the present invention provides the following technical solutions: a mechanical parking equipment control method based on cloud computing comprises the following steps:
step S100: collecting all user information entering a garage by using a camera, confirming and marking the vehicle taking behaviors of the user to form a user set, setting any user in the user set as a target user, collecting video data of the target user under a certain time sequence based on the target user, and intercepting video images at regular time by using a time round algorithm to form an image set; collecting all the vehicle taking opening positions in the mechanical parking garage to form a vehicle opening position set; meanwhile, the operation speed of the mechanical parking equipment is collected;
the step S100 includes:
s110: the method comprises the steps that a camera is used for carrying out matching screening on all collected user information entering a garage and user information of a vehicle cloud platform, and if matching is successful, user vehicle taking behaviors are confirmed and marked to form a user set A;
s120: setting any user in the user set A as a target user, acquiring video data of the target user under a certain time sequence based on the target user, and intercepting video images at intervals of t ms at regular time by using a time round algorithm to form an image set B= { B1, B2, …, bn }, wherein B1, B2, …, bn represent the image data of the target user at the 1 st, 2 nd, … th and n th time points; the time round algorithm belongs to a conventional technical means of a person skilled in the art, so that excessive details are not made in the application;
intercepting monitoring video data by utilizing a time round algorithm to form image data, so that the method is beneficial to analyzing the moving position of a user according to the image later;
s130: collecting all vehicle taking opening positions in the mechanical parking garage to form a vehicle opening position set C= { (x 1, y 1), (x 2, y 2), …, (xm, ym) }, wherein (x 1, y 1), (x 2, y 2), …, (xm, ym) represents the positions of vehicle taking openings 1,2, … and m; and meanwhile, the operation speed of the mechanical parking equipment is v.
Step S200: acquiring all acquired data and storing the acquired data;
step S200 includes: and acquiring all acquired data by utilizing a database and storing the acquired data.
Step S300: analyzing the walking speed and the real-time position of the target user according to the image set, predicting the position of the target user at the next time point based on the walking speed and the real-time position, and setting the predicted position point; based on the predicted position points and the vehicle mouth position sets, analyzing the time length of the target user reaching each vehicle mouth position to form a user time length set; according to the matching vehicle information and the vehicle position of a target user, setting the vehicle as a target vehicle, analyzing the time length of the target vehicle running to each vehicle taking position based on the target vehicle position and the vehicle opening position set, forming a vehicle time length set, and further selecting an optimal vehicle taking opening according to the user time length set and the vehicle time length set;
step S300 includes:
s310: acquiring an image set B, and performing pixel fusion on n image data by using a coincidence algorithm to form new image data bn+1; based on the image data bn+1 and the image set B, comparing pixel characteristics of any image data bz and image data bn+1 in the image set B, screening different pixels generated in the image data bn+1, constructing a two-dimensional plane coordinate system according to the screened pixels, and forming a user position set B' = { (p 1, q 1), (p 2, q 2), …, (pn, qn) }, wherein, (p 1, q 1), (p 2, q 2), …, (pn, qn) represents pixel positions of the 1 st, 2 nd, … th and n th time point target users; according to the distance di= v [ (pi+1-pi) from (pi+1, qi+1) to any position (pi, qi) in the user position set B 2 +(qi+1-qi) 2 ]Wherein i=1, 2, …, n-1, and time t gives the walking speed of the target user v= (d1+d2+ … +dn)/nt; the coincidence algorithm can carry out pixel fusion on the image textures with the same pixel points, and is beneficial to image analysis;
the image is subjected to pixel fusion by utilizing a coincidence algorithm, the generated new pixel points are used as the user moving position points, a two-dimensional plane coordinate system is constructed, the walking speed of the user is confirmed according to the pixel point distance and the duration, the confirmation of the current speed and the current position of the user is facilitated, and the prediction of the position of the next time point of the user is facilitated;
s320: and (3) performing straight line fitting based on the user position set B' to obtain a fitting straight line equation: qi=fb+ (hb) pi, where hb, fb are the slope and intercept, respectively, after straight line fitting; based on the fitting linear equation qi, predicting the position of the target user at the n+1th time point, and setting the position as a predicted position point (pn+1, qn+1); distance h between arbitrary vehicle mouth position (xf, yf) and predicted position point (pn+1, qn+1) according to vehicle mouth position set C 1 f=√[(pn+1-xf) 2 +(qn+1-yf) 2 ]Obtain the user distance set h1= { H 1 1,h 1 2,…,h 1 m }, wherein，h 1 1,h 1 2,…,h 1 m represents the distance from the target user to the 1 st, 2 nd, … th and m th vehicle taking openings, and according to any distance H in the user distance set H1 1 f and walking speed v to obtain user time length t 1 f=h 1 f/v, further obtaining a user duration set: t1= { T 1 1,t 1 2,…,t 1 m }, where t 1 1,t 1 2,…,t 1 m represents the duration from the target user to the 1 st, 2 nd, … th and m pick-up openings;
the method comprises the steps of performing straight line fitting on all position points, predicting the position of a user according to a straight line equation, analyzing the time length of the user reaching a vehicle taking port according to each vehicle taking port position, and combining the time length of the vehicle reaching the vehicle taking port position, so that the subsequent selection of the vehicle taking port information is facilitated, and the accuracy of data analysis is improved;
s330: the vehicle cloud platform is used for matching user vehicle information according to a target user, setting the user vehicle information as a target vehicle, obtaining the position of the target vehicle as (C, g) by using a positioning algorithm, and respectively obtaining the distance h from the target vehicle to each vehicle taking position based on the target vehicle position and a vehicle opening position set C 2 f=√[(c-xf) 2 +(g-yf) 2 ]Form a vehicle distance set h2= { H 2 1,h 2 2,…,h 2 m }, wherein h 2 1,h 2 2,…,h 2 m represents the distance from the target vehicle to the 1 st, 2 nd, … th and m th vehicle taking openings; based on the vehicle distance set H2 and the operating speed v of the mechanical parking device, a vehicle duration set is obtained: t2= { T 2 1,t 2 2,…,t 2 m }, where t 2 1,t 2 2,…,t 2 m represents the duration from the target vehicle to the 1 st, 2 nd, … th and m vehicle taking ports;
s340: acquiring a user duration set T1 and a vehicle duration set T2, and calculating a vehicle mouth loss score index kf= (T) based on any f-th vehicle taking mouth 1 f+t 2 f) 2 -(t 1 f-t 2 f) 2 Traversing time duration sets T1, T2 to obtain a score set k= { K1, K2 …, km }, wherein K1, K2, …, km represent loss scores from a target user to the 1 st, 2 nd, … th and m th vehicle openings, and based on the score set K, raising the loss score index kf by using a bubbling sequencing algorithmThe priority of the corresponding vehicle taking port to the target user is gradually reduced by sequential processing, if the loss score is lower, the time for the user to reach the corresponding vehicle taking port and wait for the vehicle to reach is indicated to be shorter, the possibility for the target user to select the vehicle taking port is higher, and the vehicle taking port is more convenient for the user; the vehicle pick-up positions are ordered based on the preference level to obtain a vehicle pick-up set C = { (x 1', y 1'), (x 2', y 2'), …, (xm ', ym') }, wherein (x 1', y 1'), (x 2', y 2') …, (xm ', ym') represent the 1 st, 2 nd, … th and m vehicle pick-up positions with gradually decreasing preference level of the target user.
The time length of the user reaching the vehicle taking port is compared with the time length of the vehicle reaching the vehicle taking port, the time length loss of the vehicle reaching the vehicle taking port is judged by using the vehicle port loss score index, the preference level of the user by each vehicle taking port is confirmed according to the time length loss, the vehicle taking port positions are further ordered according to the preference level, the selection of the user on the vehicle taking port is facilitated, the time of the user for taking the vehicle is greatly reduced, and the use feeling of the user on the mechanical equipment is improved;
step S400: confirming the optimal vehicle taking port positions of all users in the user set, judging whether the positions overlap, and if the positions do not overlap, respectively controlling all target vehicles to reach the optimal vehicle taking port positions in real time; if overlapping occurs, extracting overlapping user information, intelligently distributing the vehicle taking ports of the overlapping users according to the time difference of the overlapping users reaching the vehicle taking port positions, and controlling the target vehicles to reach the vehicle taking port positions in real time;
step S400 includes:
s410: determining the optimal vehicle taking position (x 1', y 1') of the target user, traversing the user set A, respectively obtaining the optimal vehicle taking positions of all users according to the step S300 to form an optimal vehicle taking position set E, simultaneously comparing the vehicle taking position set C with the optimal vehicle taking position set E, and screening the non-optimal vehicle taking positions to form a non-optimal vehicle taking position set W; traversing the optimal vehicle taking port position set E, judging whether repeated optimal vehicle taking port positions exist or not, and if the repeated vehicle taking port positions do not exist, respectively controlling target vehicles of all users to reach the optimal vehicle taking port positions in real time;
s420: if the repeated optimal vehicle taking port positions exist, extracting the optimal vehicle taking port positions (xr ', yr') which are arbitrarily repeated, further extracting beta pieces of user information which are repeatedly generated, respectively acquiring time points when any user arrives at a predicted position point and time periods when the any user arrives at the (xr ', yr') based on the beta pieces of user information, obtaining time points when the any user arrives at the optimal vehicle taking port positions (xr ', yr'), and forming a time point set S= { S1, S2, …, S beta }, wherein S1, S2, … and S beta represent time points when the 1 st, 2 nd, … th and beta users which are repeatedly generated arrive at the optimal vehicle taking port positions; and according to the score set K and the non-optimal vehicle opening set W of any user, the inferior vehicle opening positions of the 2 nd, 3 rd, … th and beta th users are distributed intelligently in sequence, and target vehicles of all users are controlled to reach the corresponding vehicle opening positions in real time.
Whether the optimal vehicle taking port positions are repeated or not is judged for all users entering the garage, and according to different results, intelligent control is carried out on the selection of the vehicles reaching each vehicle taking port position, so that the intelligence of the system is improved, the vehicle taking time length is shortened due to the fact that the users wait for a long time, and convenience for the users is facilitated.
Step S500: and displaying and reminding the route of the user to the position of each vehicle taking port by using the mobile phone terminal.
Step S500 includes: and displaying and reminding the route of the user reaching each vehicle taking opening position by using the mobile phone terminal, and monitoring in real time until the user successfully takes the vehicle.
Example 1: the step S100 includes:
s110: the method comprises the steps that a camera is used for carrying out matching screening on all collected user information entering a garage and user information of a vehicle cloud platform, and if matching is successful, user vehicle taking behaviors are confirmed and marked to form a user set A;
s120: setting any user in the user set A as a target user, acquiring video data of the target user under a certain time sequence based on the target user, and intercepting video images every 1ms at regular time by using a time round algorithm to form an image set B= { B1, B2, …, B10}, wherein B1, B2, …, B10 represent the image data of the target user at the 1 st, 2 nd, … th and 10 th time points;
s130: collecting all vehicle taking opening positions in the mechanical parking garage to form a vehicle opening position set C= { (x 1, y 1), (x 2, y 2), (x 3, y 3) }, wherein (x 1, y 1), (x 2, y 2), (x 3, y 3) represents the positions of 1 st, 2 nd and 3 rd vehicle taking openings; and simultaneously, the operation speed of the mechanical parking equipment is v=1m/s.
Step S200 includes: and acquiring all acquired data by utilizing a database and storing the acquired data.
Step S300 includes:
s310: acquiring an image set B, and performing pixel fusion on 10 image data by using a coincidence algorithm to form new image data B11; comparing the pixel characteristics of any image data bz and image data B11 in the image set B based on the image data B11 and the image set B, screening different pixels generated in the image data B11, and constructing a two-dimensional plane coordinate system according to the screened pixels to form a user position set B' = { (p 1, q 1), (p 2, q 2), …, (p 10, q 10) }, wherein (p 1, q 1), (p 2, q 2), …, (p 10, q 10) represents the pixel positions of the 1 st, 2 nd, … th and 10 th time point target users; according to the distance di= v [ (pi+1-pi) from (pi+1, qi+1) to any position (pi, qi) in the user position set B 2 +(qi+1-qi) 2 ]Where i=1, 2, …,9, and time t gives the walking speed of the target user v= (2+2+ … +2)/10=2 m/s; the coincidence algorithm can carry out pixel fusion on the image textures with the same pixel points, and is beneficial to image analysis;
s320: and (3) performing straight line fitting based on the user position set B' to obtain a fitting straight line equation: qi=fb+ (hb) pi=2+2x, where hb=fb=2 is the slope and intercept, respectively, after straight line fitting; based on the fitting straight line equation qi, predicting the position of the target user at the n+1th time point, setting as a predicted position point (p 11, q 11) = (20, 42); distance h between any vehicle mouth position (xf, yf) and predicted position point (20, 42) according to vehicle mouth position set C 1 f=√[(20-xf) 2 +(42-yf) 2 ]Obtain the user distance set h1= { H 1 1,h 1 2,h 1 3 = {16,20,18}, where h 1 1,h 1 2,h 1 3 represents the distance from the target user to the 1 st, 2 nd and 3 rd vehicle taking openings, and any distance H in the user distance set H1 is used 1 f and walkThe speed v obtains the user time t 1 f=h 1 f/2, further obtaining a user duration set: t1= { T 1 1,t 1 2,t 1 3} = {8,10,9} s, where t 1 1,t 1 2,t 1 3 represents the duration from the target user to the 1 st, 2 nd and 3 rd vehicle taking ports;
s330: the user vehicle information is matched according to the target user in the vehicle cloud platform, the user vehicle information is set as a target vehicle, the target vehicle position (15, 15) is obtained through a positioning algorithm, and the distance h from the target vehicle to each vehicle taking position is obtained based on the target vehicle position and the vehicle opening position set C 2 f=√[(15-xf) 2 +(15-yf) 2 ]Form a vehicle distance set h2= { H 2 1,h 2 2,h 2 3 = {18,16,17}, where h 2 1,h 2 2,h 2 3 represents the distance from the target vehicle to the 1 st, 2 nd and 3 rd vehicle taking openings; based on the vehicle distance set H2 and the operating speed v=1 of the mechanical parking device, a vehicle duration set is obtained: t2= { T 2 1,t 2 2,t 2 m = {18,16,17}, where t 2 1,t 2 2,…,t 2 m represents the duration from the target vehicle to the 1 st, 2 nd, … th and m vehicle taking ports;
s340: acquiring a user duration set T1 and a vehicle duration set T2, and calculating a vehicle mouth loss score index kf= (T) based on any f-th vehicle taking mouth 1 f+t 2 f) 2 -(t 1 f-t 2 f) 2 Traversing the duration sets T1 and T2 to obtain a score set K= { K1, K2 and km }, wherein K1, K2 and K3 represent the loss scores from the target user to the 1 st, 2 nd and 3 rd vehicle openings, and based on the score set K, carrying out ascending processing on the loss score index kf by using an bubbling sequencing algorithm, gradually reducing the preferential level of the corresponding vehicle opening to the target user, and if the loss score is lower, representing that the shorter the time for the user to reach the corresponding vehicle opening and wait for the vehicle to reach is, the more likely the target user selects the vehicle opening is, and the more convenient is provided for the user; based on the preference level, the vehicle pick-up positions are ordered to obtain a vehicle pick-up set c= { (x 1', y 1'), (x 2', y 2'), (…, (x 3', y 3') }, wherein (x 1', y 1'), (x 2', y 2'), (…), (x 3', y 3') represent the 1 st, 2 nd, … th and m vehicle pick-up positions with gradually reduced preference level of the target userAnd (5) placing.
Step S400 includes:
s410: determining the optimal vehicle taking position (x 1', y 1') of the target user, traversing the user set A, respectively obtaining the optimal vehicle taking positions of all users according to the step S300 to form an optimal vehicle taking position set E, simultaneously comparing the vehicle taking position set C with the optimal vehicle taking position set E, and screening the non-optimal vehicle taking positions to form a non-optimal vehicle taking position set W; traversing the optimal vehicle taking port position set E, judging whether repeated optimal vehicle taking port positions exist or not, and respectively controlling target vehicles of all users to reach the optimal vehicle taking port positions in real time if the repeated vehicle taking port positions do not exist;
step S500 includes: and displaying and reminding the route of the user reaching each vehicle taking opening position by using the mobile phone terminal, and monitoring in real time until the user successfully takes the vehicle.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A mechanical parking equipment control method based on cloud computing is characterized in that: the method comprises the following steps:
step S100: collecting all user information entering a garage by using a camera, confirming and marking the vehicle taking behaviors of the user to form a user set, setting any user in the user set as a target user, collecting video data of the target user under a certain time sequence based on the target user, and intercepting video images at regular time by using a time round algorithm to form an image set; collecting all the vehicle taking opening positions in the mechanical parking garage to form a vehicle opening position set; meanwhile, the operation speed of the mechanical parking equipment is collected;
step S200: acquiring all acquired data and storing the acquired data;
step S300: analyzing the walking speed and the real-time position of the target user according to the image set, predicting the position of the target user at the next time point based on the walking speed and the real-time position, and setting the predicted position point; based on the predicted position points and the vehicle mouth position sets, analyzing the time length of the target user reaching each vehicle mouth position to form a user time length set; according to the matching vehicle information and the vehicle position of a target user, setting the vehicle as a target vehicle, analyzing the time length of the target vehicle running to each vehicle taking position based on the target vehicle position and the vehicle opening position set, forming a vehicle time length set, and further selecting an optimal vehicle taking opening according to the user time length set and the vehicle time length set;
step S400: confirming the optimal vehicle taking port positions of all users in the user set, judging whether the positions overlap, and if the positions do not overlap, respectively controlling all target vehicles to reach the optimal vehicle taking port positions in real time; if overlapping occurs, extracting overlapping user information, intelligently distributing the vehicle taking ports of the overlapping users according to the time difference of the overlapping users reaching the vehicle taking port positions, and controlling the target vehicles to reach the vehicle taking port positions in real time;
step S500: and displaying and reminding the route of the user to the position of each vehicle taking port by using the mobile phone terminal.
2. The mechanical parking equipment control method based on cloud computing as claimed in claim 1, wherein: the step S100 includes:
s110: the method comprises the steps that a camera is used for carrying out matching screening on all collected user information entering a garage and user information of a vehicle cloud platform, and if matching is successful, user vehicle taking behaviors are confirmed and marked to form a user set A;
s120: setting any user in the user set A as a target user, acquiring video data of the target user under a certain time sequence based on the target user, and intercepting the video image every tms at regular time by using a time round algorithm to form an image set B= { B1, B2, …, bn }, wherein B1, B2, …, bn represent the image data of the target user at the 1 st, 2 nd, … th and n th time points;
s130: collecting all vehicle taking opening positions in the mechanical parking garage to form a vehicle opening position set C= { (x 1, y 1), (x 2, y 2), …, (xm, ym) }, wherein (x 1, y 1), (x 2, y 2), …, (xm, ym) represents the positions of vehicle taking openings 1,2, … and m; and meanwhile, the operation speed of the mechanical parking equipment is v.
3. The mechanical parking equipment control method based on cloud computing as claimed in claim 2, wherein: the step S300 includes:
s310: acquiring an image set B, and performing pixel fusion on n image data by using a coincidence algorithm to form new image data bn+1; based on the image data bn+1 and the image set B, comparing pixel characteristics of any image data bz and image data bn+1 in the image set B, screening different pixels generated in the image data bn+1, constructing a two-dimensional plane coordinate system according to the screened pixels, and forming a user position set B' = { (p 1, q 1), (p 2, q 2), …, (pn, qn) }, wherein, (p 1, q 1), (p 2, q 2), …, (pn, qn) represents pixel positions of the 1 st, 2 nd, … th and n th time point target users; according to the distance di= [ (pi+1-pi) from (pi+1, qi+1) to any position in the user position set B 2 +(qi+1-qi) 2 ] 1/2 Wherein i=1, 2, …, n-1, and time t gives the walking speed of the target user v= (d1+d2+ … +dn)/nt;
s320: and (3) performing straight line fitting based on the user position set B' to obtain a fitting straight line equation: qi=fb+ (hb) pi, where hb, fb are the slope and intercept, respectively, after straight line fitting; based on the fitting linear equation qi, predicting the position of the target user at the n+1th time point, and setting the position as a predicted position point (pn+1, qn+1); distance h between arbitrary vehicle mouth position (xf, yf) and predicted position point (pn+1, qn+1) according to vehicle mouth position set C 1 f=[(pn+1-xf) 2 +(qn+1-yf) 2 ] 1/2 Obtain the user distance set h1= { H 1 1,h 1 2,…,h 1 m }, wherein h 1 1,h 1 2,…,h 1 m represents the distance from the target user to the 1 st, 2 nd, … th and m th vehicle taking openings, and according to any distance H in the user distance set H1 1 f and walking speed vTo the user time length t 1 f=h 1 f/v, further obtaining a user duration set: t1= { T 1 1,t 1 2,…,t 1 m }, where t 1 1,t 1 2,…,t 1 m represents the duration from the target user to the 1 st, 2 nd, … th and m pick-up openings;
s330: the vehicle cloud platform is used for matching user vehicle information according to a target user, setting the user vehicle information as a target vehicle, obtaining the position of the target vehicle as (C, g) by using a positioning algorithm, and respectively obtaining the distance h from the target vehicle to each vehicle taking position based on the target vehicle position and a vehicle opening position set C 2 f=[(c-xf) 2 +(g-yf) 2 ] 1/2 Form a vehicle distance set h2= { H 2 1,h 2 2,…,h 2 m }, wherein h 2 1,h 2 2,…,h 2 m represents the distance from the target vehicle to the 1 st, 2 nd, … th and m th vehicle taking openings; based on the vehicle distance set H2 and the operating speed v of the mechanical parking device, a vehicle duration set is obtained: t2= { T 2 1,t 2 2,…,t 2 m }, where t 2 1,t 2 2,…,t 2 m represents the duration from the target vehicle to the 1 st, 2 nd, … th and m vehicle taking ports;
s340: acquiring a user duration set T1 and a vehicle duration set T2, and calculating a vehicle mouth loss score index kf= (T) based on any f-th vehicle taking mouth 1 f+t 2 f) 2 -(t 1 f-t 2 f) 2 Traversing the duration sets T1 and T2 to obtain score sets K= { K1, K2 … and km }, wherein K1, K2 and … are loss scores from a target user to 1 st, 2 nd, … th and m th vehicle openings, and based on the score sets K, carrying out ascending processing on loss score indexes kf by using an bubbling sequencing algorithm, so that the preferential grades of the corresponding vehicle taking openings to the target user are gradually reduced; the vehicle pick-up positions are ordered based on the preference level to obtain a vehicle pick-up set C = { (x 1', y 1'), (x 2', y 2'), …, (xm ', ym') }, wherein (x 1', y 1'), (x 2', y 2') …, (xm ', ym') represent the 1 st, 2 nd, … th and m vehicle pick-up positions with gradually decreasing preference level of the target user.
4. A method for controlling a mechanical parking device based on cloud computing as claimed in claim 3, wherein: the step S400 includes:
s410: determining the optimal vehicle taking position (x 1', y 1') of the target user, traversing the user set A, respectively obtaining the optimal vehicle taking positions of all users according to the step S300 to form an optimal vehicle taking position set E, simultaneously comparing the vehicle taking position set C with the optimal vehicle taking position set E, and screening the non-optimal vehicle taking positions to form a non-optimal vehicle taking position set W; traversing the optimal vehicle taking port position set E, judging whether repeated optimal vehicle taking port positions exist or not, and if the repeated vehicle taking port positions do not exist, respectively controlling target vehicles of all users to reach the optimal vehicle taking port positions in real time;
s420: if the repeated optimal vehicle taking port positions exist, extracting the optimal vehicle taking port positions (xr ', yr') which are arbitrarily repeated, further extracting beta pieces of user information which are repeatedly generated, respectively acquiring time points when any user arrives at a predicted position point and time periods when the any user arrives at the (xr ', yr') based on the beta pieces of user information, obtaining time points when the any user arrives at the optimal vehicle taking port positions (xr ', yr'), and forming a time point set S= { S1, S2, …, S beta }, wherein S1, S2, … and S beta represent time points when the 1 st, 2 nd, … th and beta users which are repeatedly generated arrive at the optimal vehicle taking port positions; and according to the score set K and the non-optimal vehicle opening set W of any user, the inferior vehicle opening positions of the 2 nd, 3 rd, … th and beta th users are distributed intelligently in sequence, and target vehicles of all users are controlled to reach the corresponding vehicle opening positions in real time.
5. A mechanical parking equipment control system for implementing the cloud computing-based mechanical parking equipment control method of any one of claims 1-4, characterized in that: the system comprises: the system comprises a data acquisition module, a database, a vehicle analysis module, an intelligent control module and a data feedback module;
the method comprises the steps that the data acquisition module acquires all user information entering a garage by using a camera, confirms and marks the vehicle taking behavior of a user to form a user set, any user in the user set is set as a target user, video data of the target user in a certain time sequence are acquired based on the target user, and video images are intercepted at regular time by using a time round algorithm to form an image set; collecting all the vehicle taking opening positions in the mechanical parking garage to form a vehicle opening position set; meanwhile, the operation speed of the mechanical parking equipment is collected;
acquiring all acquired data through the database and storing the acquired data;
analyzing the walking speed and the real-time position of the target user according to the image set by the vehicle analysis module, predicting the position of the target user at the next time point based on the walking speed and the real-time position, and setting the predicted position point; based on the predicted position points and the vehicle mouth position sets, analyzing the time length of the target user reaching each vehicle mouth position to form a user time length set; according to the matching vehicle information and the vehicle position of a target user, setting the vehicle as a target vehicle, analyzing the time length of the target vehicle running to each vehicle taking position based on the target vehicle position and the vehicle opening position set, forming a vehicle time length set, and further selecting an optimal vehicle taking opening according to the user time length set and the vehicle time length set;
the intelligent control module confirms the optimal vehicle taking port positions of all users in the user set, judges whether the positions overlap, and respectively controls all target vehicles to reach the optimal vehicle taking port positions in real time if the positions do not overlap; if overlapping occurs, extracting overlapping user information, intelligently distributing the vehicle taking ports of the overlapping users according to the time difference of the overlapping users reaching the vehicle taking port positions, and controlling the target vehicles to reach the vehicle taking port positions in real time;
and displaying and reminding the user of arriving at the position route of each vehicle taking port by using the mobile phone terminal through the data feedback module.
6. The cloud computing-based mechanical parking equipment control system of claim 5, wherein: the data acquisition module comprises a user acquisition unit, a video acquisition unit, a vehicle mouth position acquisition unit and an equipment speed acquisition unit;
the user acquisition unit is used for acquiring all user information entering the garage by using the camera, and confirming and marking the user vehicle taking behavior to form a user set; the video acquisition unit is used for acquiring video data of a target user under a certain time sequence, and intercepting video images at fixed time by utilizing a time round algorithm to form an image set; the vehicle opening position acquisition unit is used for acquiring all vehicle opening positions in the mechanical parking garage to form a vehicle opening position set; the equipment speed acquisition unit is used for acquiring the operation speed of the mechanical parking equipment.
7. The cloud computing-based mechanical parking equipment control system of claim 5, wherein: the vehicle analysis module comprises a position prediction unit, a user duration analysis unit, a vehicle duration analysis unit and an optimal vehicle opening selection unit;
the position prediction unit is used for analyzing the walking speed and the real-time position of the target user according to the image set, predicting the position of the target user at the next time point based on the walking speed and the real-time position, and setting the position as a predicted position point; the user duration analysis unit is used for analyzing the duration of the target user reaching each vehicle taking position based on the predicted position points and the vehicle opening position sets to form a user duration set; the vehicle duration analysis unit is used for setting a target vehicle according to the matching vehicle information of the target user and the vehicle position, and analyzing the duration of the target vehicle from running to each vehicle taking position based on the target vehicle position and the vehicle opening position set to form a vehicle duration set; the optimal vehicle opening selection unit is used for selecting an optimal vehicle taking opening according to the user duration set and the vehicle duration set.
8. The cloud computing-based mechanical parking equipment control system of claim 5, wherein: the intelligent control module comprises an overlap analysis unit, a time difference analysis unit and a real-time control unit;
the overlapping analysis unit is used for confirming the optimal vehicle taking port positions of all users in the user set and judging whether the positions overlap; the time difference analysis unit is used for extracting information of the overlapped users and intelligently distributing the vehicle taking ports of the overlapped users according to the time difference of the overlapped users reaching the vehicle taking port; the real-time control unit is used for controlling the target vehicle to reach the positions of the vehicle taking openings in real time.
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