CN114495552A - Navigation method and system for quickly parking and finding vehicle - Google Patents

Abstract

The invention discloses a navigation method and a system for quickly parking and finding a vehicle, and belongs to the technical field of intelligent parking. Acquiring vacant parking spaces and parking lot inlets in a parking layer where the current vehicle is located; taking an elevator in a parking layer where a vehicle is located as a starting point, establishing a screening model, and screening a preset number of target parking spaces in the vacant parking space set to obtain a target parking space set; a hierarchical analysis algorithm is introduced, and the optimal parking space meeting the user requirements is obtained based on the target parking space set analysis; and recommending an optimal parking path from the entrance of the parking lot to the optimal parking space to the user by adopting a path algorithm. The invention selects the target parking spaces with the preset number from the current vacant parking spaces, so that the user can park in the fastest time and leave the parking lot in the fastest time while selecting the proper parking spaces according to the preference of the user. The position of the user is tracked in real time based on software, and the user can push a car-finding navigation route for the user when finding the car by combining the best parking space pushed before, so that the user can find the car conveniently.

Description

A kind of fast parking to find a car navigation method and system thereof

technical field

The invention belongs to the technical field of intelligent parking, and in particular relates to a method and a system for quickly parking and finding a car for navigation.

Background technique

With the improvement of living standards and the rapid development of the economy, the increase in the number of family cars has brought convenience to life, but at the same time, the problem of "difficulty in parking" has also emerged. In order to solve the problem of "parking difficulty", multi-storey parking lots have become popular. At present, most business centers have multi-storey underground or above-ground parking spaces for people to use. Due to the unfamiliarity with the parking lot and the lack of parking space information of the current parking lot, car owners cannot find the best parking space in a short time. This further exacerbates the vicious circle of parking difficulties and reduces people's travel efficiency.

SUMMARY OF THE INVENTION

In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a fast parking and car-finding navigation method and a system thereof.

The present invention adopts the following technical scheme to realize: a kind of fast parking to find a car navigation method, comprising the following steps:

Obtain the vacant parking spaces in the parking layer where the current vehicle is located and the parking lot entrance to generate a vacant parking space set U;

Taking the elevator in the parking floor where the vehicle is located as a starting point, a screening model is established, and a predetermined number of target parking spaces are selected from the set U of vacant parking spaces to obtain a target parking space set C;

Analytic hierarchy process is introduced, and the optimal parking space that meets the user's needs is obtained based on the target parking space set C analysis;

A path algorithm is used to recommend the best parking path to the user from the entrance of the parking lot to the best parking space.

In a further embodiment, the following steps are also included:

A path algorithm is used to recommend the best walking path from the best parking space to the elevator to the user, generate parking information from the best parking path, the best walking path and the corresponding license plate, and send the parking information to the user's mobile phone ;

When looking for a car, the mobile phone switches to the navigation mode to recommend the walking route from the current location to the best parking space based on the parking information and the user's current location.

By adopting the above technical solution, it is convenient for users to bind the elevator parking lot, parking space and license plate in the shortest time and send it to the owner's mobile phone APP via wireless means. Navigation mode, convenient for car owners to find a car.

In a further embodiment, the establishment process of the screening model is as follows:

Each element in the vacant parking space set includes at least the following parameters: the vacant parking space node k, the distance from the corresponding vacant parking space node to the starting point;

Every time a new path length is calculated, it is compared with the threshold, and the corresponding node smaller than the threshold is upgraded to the target node;

The target node and the corresponding distance length are removed from the vacant parking space set U, and updated to the target set S, until the elements in the target set S meet the calculation stop condition.

By adopting the above technical solution, a calculation stop condition is introduced, that is, there is no need to calculate and analyze each vacant parking space node, and the target parking space can be filtered out if the calculation stop condition is satisfied, which greatly reduces the amount of calculation and shortens the calculation time of path generation.

In a further embodiment, the calculation stop condition is:

When the newly added elements in the target set S are greater than the predetermined number, the operation of the remaining elements in the vacant parking space set U is stopped;

Or, if the newly added elements in the target set S are less than the predetermined number and the parking lot entrance is transferred to the target set S as a newly added element, the calculation of the remaining elements in the vacant parking space set U is stopped.

In a further embodiment, the AHP algorithm specifically includes the following steps:

Establish a target layer Z, a criterion layer A and a scheme layer B; the scheme layer B includes a target parking space set C={c ₁ , c ₂ , c ₃ , ..., cm }, where _m represents the number of target parking spaces;

_ij 表示第i个规则的第j个属性值，决策成对比较矩阵A=（

_ij ） _m×n ，其中1≤i≤n；The criterion layer A includes n rules, denoted as G={ g ₁ , g ₂ , g ₃ , ..., g _n }, where n represents the number of rules, and is represented by

_ij represents the jth attribute value of the ith rule, and the decision pairwise comparison matrix A=(

_ij ) _m×n , where 1≤i≤n;

；Calculate the weight value of each rule in the criterion layer A corresponding to the target layer

;

；Calculate the weight value of each target parking space in the scheme layer B corresponding to each rule in the criterion layer A

;

和权重值

组合得到关于每个目标车位的组合权重

，选定组合权重

中的最大值对应的目标车位为最佳停车位。Based on weight value

and weight value

The combination gets the combined weight for each target parking space

, the selected combination weight

The target parking space corresponding to the maximum value is the optimal parking space.

In a further embodiment, it also includes checking the consistency of the pair comparison matrix in the criterion layer A and the solution layer B. Taking the pair comparison matrix A as an example, the check criterion is as follows:

_ij ） _m×n =

；Then A=(

_ij ) _m×n =

;

=

_max

，其中，

_max 为矩阵A的最大特征值，

为对应

_max 的特征向量。 A

=

_max

,in,

_max is the largest eigenvalue of matrix A,

to correspond to

The eigenvector of _max .

In a further embodiment, at least the following rules are included: whether the parking space is close to the entrance of the parking lot, whether the parking space is close to the elevator, the type of parking space, whether the two sides of the parking space are occupied, and the distance between the parking space and the exit of the parking lot.

By adopting the above technical solution, according to the user's needs and the user's parking preference, the best parking space with the elevator as the starting point is selected.

In a further embodiment, the path algorithm adopts Dijkstra's algorithm.

A fast parking and car-finding navigation system, comprising:

a mobile terminal, set at the first module, the second module, the third module and the fourth module of the mobile terminal;

Wherein, the first module is set to obtain the vacant parking spaces in the parking floor where the current vehicle is located and the entrance of the parking lot to generate a vacant parking space set U;

The second module is set to take the elevator in the parking floor where the vehicle is located as a starting point, establish a screening model, and filter a predetermined number of target parking spaces in the vacant parking space set U to obtain a target parking space set C;

The third module is set to introduce an analytic hierarchy process, and based on the target parking space set C, the analysis obtains the best parking space that meets the needs of the user;

The fourth module is set up to recommend to the user the best parking path from the entrance of the parking lot to the best parking space using a routing algorithm.

In a further embodiment, it also includes: a fifth module configured to recommend an optimal walking path from the optimal parking space to the elevator to the user by using a path algorithm, and compare the optimal parking path, the optimal walking path and the corresponding license plate to generate parking information;

a sixth module, configured to store the parking information;

The seventh module is configured to switch to the navigation mode to recommend a walking route from the current location to the best parking space to the user based on the parking information and the current location of the user.

Beneficial effects of the present invention: the present invention selects a predetermined number of target parking spaces from the current vacant parking spaces, generates an analytic hierarchy process in combination with user requirements, selects the best parking space among the predetermined number of target parking spaces, and then adopts the Dijkstra algorithm again. Guide the vehicle into the parking space, let the user choose the appropriate parking space according to their own preferences, park in the fastest time and guarantee to leave the parking lot in the fastest time. Fully reflect people-oriented, improve parking efficiency and save user time. At the same time, based on the real-time tracking of the user's location based on the software, combined with the best parking spaces pushed before, when the user is looking for a car, the navigation route for finding a car is pushed for the user, which is convenient for the user to find a car.

Description of drawings

Figure 1 is a schematic diagram of the distribution of roads and parking spaces in the parking lot.

FIG. 2 is a flow chart of a method for fast parking and car-finding navigation.

Fig. 3 is the structure model diagram of analytic hierarchy process.

Figure 4 is a simulation diagram of the optimal parking space.

Detailed ways

The present invention will be further described below with reference to the embodiments and accompanying drawings.

The underground parking lot of a large complex has several floors. When driving to the underground parking lot, car owners usually drive by themselves to find a parking space. Usually, because it is a one-time parking, the car owner does not know the map and orientation of the parking lot, which leads to blind parking. Difficult to find a parked space. Waste a lot of time and energy.

Based on the above problems, saving time and energy is still reflected in time. In this embodiment, the shortest time is the period from when the person and the car enter the parking lot together until the person leaves the parking lot. This time includes the time from the entrance of the parking lot to the parking space and the parking time, and the time when people come out of the parking space to the exit position (the exit position here refers to the elevator entrance of the underground parking lot). The length of time people spend in the entire parking lot is directly related to the owner's journey time and work efficiency. There are two factors that determine the length of this time. One is the location of the vacant parking space; the other is the car from the parking lot entrance to the parking space and the parking time. The time for a person to arrive at the elevator entrance depends on the walking time from the vacant parking space to the elevator entrance. The walking time varies depending on the location of the parking space, so this period of time is generally static and immutable. Based on the above analysis, it can be seen that the shortest path of selection time is the selection of free parking spaces and the shortest time of the parking guidance system.

Example 1

As shown in FIG. 1 , P1-P8 in the figure represent free parking spaces. Among the above free parking spaces, there are parking spaces with occupied parking spaces on both sides or occupied parking spaces on one side, and parking spaces with different distances from the elevator. This embodiment discloses a quick parking and car-finding navigation method, as shown in FIG. 2 , including the following steps:

Obtain the vacant parking spaces and parking lot entrances in the parking layer where the current vehicle is located to generate a vacant parking space set U; each vacant parking space and parking lot entrance are abstracted into a vacant parking space node and an end node respectively. Wherein, each element in the vacant parking space set U at least includes the following parameters: the vacant parking space node k, and the distance from the corresponding vacant parking space node to the starting point. That is, each vacant parking space includes parameters: the vacant parking space node k, and the distance from the corresponding vacant parking space node to the starting point. k represents the number of vacant parking spaces, and in this embodiment, 1≤k≤8.

Taking the elevator in the parking floor where the vehicle is located as the starting point, a screening model is established, and a predetermined number of target parking spaces are screened out from the spare parking space set U to obtain the target parking space set C = {c ₁ , c ₂ , c ₃ , ..., c _m }, where m represents the number of target parking spaces. In this embodiment, the value of the predetermined number is 3, that is, m=3. In other words, three vacant parking spaces that meet the demand are selected from the vacant parking space set U as target parking spaces.

Based on the above description, the user does not know much about the parking lot information, and there is information asymmetry between the user's subjective wishes and the vacant parking spaces in the parking lot. It is often only after the user completes the parking that they find out that there is a better parking space to meet their personal needs for parking. It is very important for users to choose parking spaces according to their needs at the time. Therefore, the choice of introducing users' personal preferences into vacant parking spaces highlights the humanization and efficiency of parking.

Therefore, in this embodiment, an AHP algorithm is introduced to analyze and obtain the optimal parking space that meets the user's needs based on the target parking space set C; the path algorithm is used to recommend to the user the optimal parking space from the entrance of the parking lot to the optimal parking space path.

By adopting the above technical solution, the user's preference is taken into account in the information release in the route planning, so that the user can choose a suitable parking space according to his or her preference.

In order to allow the user to walk from the best parking space to the elevator in the shortest time, the Dijkstra path algorithm is adopted (the Dijkstra path algorithm here is a relatively common algorithm in the prior art, which will not be repeated here.) Best walking path from parking space to elevator. At the same time, in order to provide convenience for the user to find a car when leaving, parking information is generated from the optimal parking path, the optimal walking path and the corresponding license plate, and the parking information is sent to the user's mobile phone.

When the user's business is finished or the user needs to find a car, the mobile phone switches to the navigation mode based on the parking information and the user's current location to recommend the walking route from the current location to the best parking space to the user. In this embodiment, the walking route, the optimal parking path, and the optimal walking path are all displayed to the user in the form of a map, which is convenient for the user to obtain information more intuitively. Use the fastest path to find the parking space where the vehicle is located, reduce the possibility of getting lost due to unfamiliarity with the parking lot, and improve the efficiency of vehicle search.

The existing shortest path research methods in parking lot guidance systems mainly include Dijkstra algorithm, ant colony algorithm, particle swarm algorithm and heuristic search algorithm, etc. Dijkstra algorithm is very practical in finding the shortest path in a weighted directed graph. Value, in the path planning of the parking lot, the entrance and exit of the parking lot and the location of the elevator are the determined location points, which meet the algorithm solution requirements. The traditional Dijkstra algorithm is calculated by adding the shortest path weights from the starting node to the remaining nodes one by one into the shortest path tree one by one and finding the shortest path from the starting point to any node. In the parking guidance system, the traditional Dijdktra takes the entrance of the parking lot as the starting point and the exit as the end point, and searches for the shortest path for parking. If it is applied in a parking lot with a large number of nodes, the calculation amount is large and the efficiency is very low.

This parking scheme has two problems: First, for users, all parking spaces are the same, as long as they can park with the entrance as the shortest path, the actual needs of users are not considered. Moreover, the traditional Dijkstra algorithm is to traverse and sort all the free parking spaces from the entrance to the exit, which wastes a lot of time and resources, and the shortest path parking spaces found may not necessarily meet the needs of users. Therefore, this embodiment has made the following improvements:

The establishment process of the screening model is as follows: each element in the vacant parking space set includes at least the following parameters: the vacant parking space node k, and the distance from the corresponding vacant parking space node to the starting point;

Every time a new path length is calculated, it is compared with the threshold, and the corresponding node smaller than the threshold is upgraded to the target node.

The target node and the corresponding distance length are removed from the vacant parking space set U, and updated to the target set S, until the elements in the target set S meet the calculation stop condition. By introducing the calculation stop condition, the number of nodes is reduced, the calculation amount is small, and the bootstrap efficiency is improved.

In a further embodiment, the calculation stop condition is: when the newly added elements in the target set S are greater than a predetermined number, the calculation of the remaining elements in the vacant parking space set U is stopped; or, the newly added elements in the target set S are If the number of elements is less than the predetermined number and the parking lot entrance is transferred to the target set S as a newly added element, the operation of the remaining elements in the vacant parking space set U is stopped.

For example: a vacant parking space node v in the vacant parking space set U is not adjacent to the starting point s, then the distance from v to the starting point s is ∞, and the initial value i=0 of the number of elements in the target set S is recorded.

When a vacant parking space node v in the vacant parking space set U is adjacent to the starting point s, and the calculated distance length is less than the threshold (in this embodiment, the threshold is set in advance according to the actual situation of the parking lot), then the vacant parking space Node v is upgraded to a target node; the target node and the corresponding journey length are removed from the vacant parking space set U, and updated to the target set S, and the initial value i+1 of the number of elements in the target set S is recorded at the same time. This is repeated until i=3 or the target set S already has a parking lot entrance.

The stop condition is whether the target node reaches 3 or not reaches 3 and the parking lot entrance node has entered the shortest path as the condition for the operation to stop. Therefore, the amount of computation is greatly reduced.

In a further embodiment, as shown in Figure 3, the AHP algorithm specifically includes the following steps:

Establish a target layer Z, a criterion layer A and a scheme layer B; the scheme layer B includes a target parking space set C={c ₁ , c ₂ , c ₃ , ..., cm }, where _m represents the number of target parking spaces;

_ij 表示第i个规则的第j个属性值，决策成对比较矩阵A=（

_ij ） _m×n ，其中1≤i≤n；所述规则进一步为车位是否靠近停车场入口、车位是否靠近电梯、车位类型、车位的两侧是否占用、车位与停车场出口之间的距离。The criterion layer A includes n rules, denoted as G={ g ₁ , g ₂ , g ₃ , ..., g _n }, where n represents the number of rules, and is represented by

_ij represents the jth attribute value of the ith rule, and the decision pairwise comparison matrix A=(

_ij ) _m×n , where 1≤i≤n; the rules further include whether the parking space is close to the entrance of the parking lot, whether the parking space is close to the elevator, the type of parking space, whether the two sides of the parking space are occupied, and the distance between the parking space and the exit of the parking lot.

；Calculate the weight value of each rule in the criterion layer A corresponding to the target layer

;

；Calculate the weight value of each target parking space in the scheme layer B corresponding to each rule in the criterion layer A

;

和权重值

组合得到关于每个目标车位的组合权重

，选定组合权重

中的最大值对应的目标车位为最佳停车位。该最佳停车位不仅仅是位置和时间上的最佳，同时符合了用户的需求，增加停车的舒适感。Based on weight value

and weight value

The combination gets the combined weight for each target parking space

, the selected combination weight

The target parking space corresponding to the maximum value is the optimal parking space. The optimal parking space is not only the best in terms of location and time, but also meets the needs of users and increases the comfort of parking.

It also includes the consistency test of the pair comparison matrix in the criterion layer A and the scheme layer B. Taking the pair comparison matrix A as an example, the test criteria are as follows:

_ij ） _m×n =

；Then A=(

_ij ) _m×n =

;

=

_max

，其中，

_max 为矩阵A的最大特征值，

为对应

_max 的特征向量。 A

=

_max

,in,

_max is the largest eigenvalue of matrix A,

to correspond to

The eigenvector of _max .

Example: According to the parking lot model established in Figure 1, considering the needs of users, the matrix is determined as:

，采筛选模型筛选出三个目标停车位P8，P5和P6，基于上述公式，采用归一法计算出准则层A中的每个规则对应于目标层的权重值

，分别为0.1263、0.5495、0.2476和0.0736。A =

, using the screening model to screen out three target parking spaces P8, P5 and P6, based on the above formula, the normalization method is used to calculate the weight value of each rule in the criterion layer A corresponding to the target layer

, 0.1263, 0.5495, 0.2476, and 0.0736, respectively.

，如表示1所示:Similarly, calculate the weight value of each target parking space in the scheme layer B corresponding to each rule in the criterion layer A

, as shown in Representation 1:

Table 1

A1 A2 A3 A4 B1 0.6370 0.5816 0.1396 0.1095 B2 0.2583 0.3090 0.5278 0.3090 B3 0.1047 0.1095 0.3325 0.5816

Finally, the combined weights of the three parking schemes are calculated as: 0.445, 0.3567 and 0.199, corresponding to the weights of P8, P5 and P6 respectively. According to the analytic hierarchy process, P8 is the optimal parking space for the user. It can be seen from Figure 4 that the optimal parking space is P8. The simulation results are consistent with the actual situation.

This embodiment combines the user's decision on the demand for parking spaces with the shortest search path of the existing parking spaces. On the one hand, it is convenient for the needs of different users, and on the other hand, it is conducive to the guidance of parking. The distance is the shortest, which improves the parking efficiency of users and the utilization rate of parking spaces in the underground parking lot.

Example 2

This embodiment discloses a fast parking and car-finding navigation system, which is used to implement the method described in Embodiment 1. Including: a mobile terminal, a first module, a second module, a third module and a fourth module arranged on the mobile terminal;

Wherein, the first module is set to obtain the vacant parking spaces in the parking floor where the current vehicle is located and the parking lot entrance to generate a vacant parking space set U;

The second module is set to take the elevator in the parking floor where the vehicle is located as a starting point, establish a screening model, and select a predetermined number of target parking spaces in the vacant parking space set U to obtain a target parking space set C;

The third module is set to introduce an analytic hierarchy process, and based on the target parking space set C, the analysis obtains the best parking space that meets the needs of the user;

The fourth module is set up to recommend to the user the best parking path from the entrance of the parking lot to the best parking space using a routing algorithm.

It also includes: a fifth module, configured to use a path algorithm to recommend the best walking path from the best parking space to the elevator to the user, and generate parking information from the best parking path, the best walking path and the corresponding license plate;

a sixth module, configured to store the parking information;

The seventh module is configured to switch to the navigation mode to recommend a walking route from the current location to the best parking space to the user based on the parking information and the current location of the user.

Claims (10)

1. A quick parking and vehicle finding navigation method is characterized by comprising the following steps:

acquiring vacant parking spaces in a parking layer where a current vehicle is located and generating a vacant parking space set U by a parking lot entrance;

taking an elevator in a parking layer where a vehicle is located as a starting point, establishing a screening model, and screening a preset number of target parking spaces from the vacant parking space set U to obtain a target parking space set C;

a hierarchical analysis algorithm is introduced, and the optimal parking space meeting the user requirements is obtained based on the analysis of the target parking space set C;

and recommending an optimal parking path from the entrance of the parking lot to the optimal parking space to the user by adopting a path algorithm.

2. The quick parking and vehicle finding navigation method according to claim 1, further comprising the steps of:

recommending an optimal walking path from an optimal parking space to an elevator to a user by adopting a path algorithm, generating parking information by the optimal parking path, the optimal walking path and a corresponding license plate, and sending the parking information to a mobile phone of the user;

when finding the car, the mobile phone is switched to the navigation mode to recommend a walking route from the current position to the optimal parking space to the user based on the parking information and the current position of the user.

3. The navigation method for fast parking and finding the vehicle according to claim 1, wherein the screening model is established as follows:

each element in the set of vacant parking spaces at least comprises the following parameters: the distance from the vacant parking space node k to the starting point corresponds to the vacant parking space node k;

comparing the new path length obtained by calculation with a threshold value every time, and upgrading the corresponding node smaller than the threshold value into a target node;

and removing the target nodes and the corresponding path lengths from the vacant parking space set U, and updating the target nodes and the corresponding path lengths into a target set S until elements in the target set S meet the calculation stop conditions.

4. The quick parking and car finding navigation method according to claim 3, wherein the calculation stopping condition is that:

when the number of the newly added elements in the target set S is larger than the preset number, stopping the operation on the remaining elements in the vacant parking space set U;

or if the newly added elements in the target set S are less than the preset number and the parking lot entrance is transferred to the target set S as the newly added elements, the operation on the residual elements in the vacant parking space set U is stopped.

5. The navigation method for fast parking and finding the vehicle according to claim 1, wherein the hierarchical analysis algorithm specifically comprises the following steps:

establishing a target layer Z, a criterion layer A and a scheme layer B; the scheme layer B comprises a target parking space set C = { C = { (C) }₁，c₂，c₃，…，c_mM represents the number of target parking spaces;

the criterion layer A comprises n rules, andis G = { G₁，g₂，g₃，…，g_nWhere n denotes the number of rules, with

_ij The j attribute value representing the i rule is decided as the comparison matrix A = (C) ((C))

_ij ） _m×n Wherein i is more than or equal to 1 and less than or equal to n;

calculating weight value of each rule in criterion layer A corresponding to target layer

；

Respectively calculating the weight value of each target parking space in the scheme layer B corresponding to each rule in the criterion layer A

；

Based on weight value

And weight value

Combining to obtain a combined weight for each target parking space

Selecting combining weights

The target parking space corresponding to the maximum value in the parking space is the optimal parking space.

6. The navigation method for finding the vehicle for the fast parking according to claim 5, further comprising a consistency check of comparison matrixes in the criterion layer A and the scheme layer B, wherein the check criteria are as follows by taking the comparison matrix A as an example:

then a =: (

_ij ） _m×n =

；

A

=

_max

Wherein, in the step (A),

_max is the largest eigenvalue of the matrix a,

to correspond to

_max The feature vector of (2).

7. The quick parking and vehicle finding navigation method according to claim 5, characterized by comprising at least the following rules: whether the parking space is close to the entrance of the parking lot, whether the parking space is close to the elevator, the type of the parking space, whether the two sides of the parking space occupy and the distance between the parking space and the exit of the parking lot.

8. The quick parking and vehicle finding navigation method according to any one of claims 1 or 2, characterized in that the path algorithm adopts Dijkstra algorithm.

9. A quick parking and car finding navigation system is characterized by comprising:

the mobile terminal comprises a mobile terminal, a first module, a second module, a third module and a fourth module, wherein the mobile terminal is arranged on the first module, the second module, the third module and the fourth module of the mobile terminal;

the first module is set to acquire vacant parking spaces in a parking layer where a current vehicle is located and a parking lot entrance to generate a vacant parking space set U;

the second module is set to establish a screening model by taking an elevator in a parking layer where a vehicle is located as a starting point, and a target parking space set C is obtained by screening a preset number of target parking spaces in the vacant parking space set U;

the third module is set to introduce a hierarchical analysis algorithm, and the optimal parking space meeting the user requirement is obtained based on the analysis of the target parking space set C;

the fourth module is configured to recommend an optimal parking path to the user from the parking lot entrance to the optimal parking space using a path algorithm.

10. The quick parking and car finding navigation system as claimed in claim 9, further comprising: the fifth module is set to recommend an optimal walking path from an optimal parking space to an elevator to a user by adopting a path algorithm, and generates parking information from the optimal parking path, the optimal walking path and a corresponding license plate;

a sixth module configured to store the parking information;

and the seventh module is set to switch to the navigation mode to recommend a walking route from the current position to the optimal parking space to the user based on the parking information and the current position of the user.

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