Multi-scene path planning method and system based on mobile terminal offline navigation

Abstract

The invention discloses a multi-scene path planning method and system based on mobile terminal off-line navigation, and the method comprises the steps: obtaining the map data of the mobile terminal off-line navigation, and generating an initial planning path according to the initial position and destination position of a user; setting an optimization model of path planning, finding out an optimal planning path, comparing the initial planning path with the optimal planning path, and when the difference between the initial planning path and the optimal planning path exceeds a preset threshold value, taking the optimal planning path as a final planning path; and pushing the final planned path to the user, and navigating by the user through the final planned path.

Description

Technical Field

[0001] The present invention belongs to the field of map navigation technology, and more specifically, relates to a multi-scenario path planning method and system based on mobile terminal offline navigation. Background Art

[0002] Mobile offline navigation technology mainly uses built-in processed domain navigation electronic maps to enable mobile devices to use external navigation locators to provide accurate and continuous navigation services without Internet support.

[0003] However, in the prior art, since the navigation is in an offline state, deviations may occur when planning the path, resulting in the problem that the optimal path cannot be determined. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a multi-scenario path planning method based on mobile terminal offline navigation, comprising:

[0005] Obtain the map data of the mobile offline navigation and generate the initial planning path according to the user's starting location and destination location;

[0006] Setting an optimization model for path planning, finding an optimal planned path, comparing the initial planned path with the optimal planned path, and when the difference between the initial planned path and the optimal planned path exceeds a preset threshold, taking the optimal planned path as the final planned path;

[0007] The final planned path is pushed to the user, and the user navigates through the final planned path.

[0008] Furthermore, the cost function of the planned path includes:

[0009]

[0010] Among them, F(P) is the cost index of the planned path P, k is the number of nodes on the planned path P, α is the first adjustment factor of the cost function, and d(n i , n i+1 ) is the i-th node n on the planned path P i The distance to the i+1th node, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the planned path P from the i-th node n i The estimated travel time of the vehicle to the i+1th node, γ is the third adjustment factor of the cost function, S i is the maximum speed limit of the road where the i-th node is located, δ is the fourth adjustment factor of the cost function, σ iis the visibility of the road where the i-th node is located, ∈ is the fifth adjustment factor of the cost function, g(T i ) is the traffic flow T of the road where the i-th node is located i The influence function on the cost function.

[0011] Furthermore, the traffic flow T of the road where the i-th node is located i The influence function g(T i )include:

[0012]

[0013] Where μ is the influence function g(T i ) is the first adjustment factor, v is the influence function g(T i ) is the second adjustment factor, v′ is the influence function g(T i ), τ is the threshold of vehicle flow.

[0014] Furthermore, the constraint functions of the planned path P include:

[0015]

[0016] Among them, G(P) is the constraint index of the planned path P, h(n i , S i ) is the node accessibility evaluation function, which is used to evaluate the maximum speed limit of S i The i-th node n on the path i The accessibility, η i For the i-th node n i The first weight of traffic flow, ζ i For the i-th node n i is the second weight of the traffic flow, θ′ is the adjustment factor of the constraint function, and C is the threshold of the constraint index.

[0017] Furthermore, the path planning dynamic adjustment function includes:

[0018]

[0019] Among them, μ′ i For the i-th node n i is the priority weight of , and ξ is the adjustment factor of the path planning dynamic adjustment function.

[0020] Furthermore, the node accessibility evaluation function h(n i , S i )include:

[0021]

[0022] Among them, α″ is the adjustment factor of the node accessibility evaluation function, F actual (S i ) is the maximum speed limit S i The current travel time of the road, F max (S i ) is the maximum speed limit S i The maximum travel time of the road, d terrain (S i ) is the maximum speed limit S i The slope of the road.

[0023] Furthermore, the optimization model of the path planning includes:

[0024] min(F(P)+D(P)stG(P)≤C).

[0025] The present invention also proposes a multi-scenario path planning system based on mobile terminal offline navigation, comprising:

[0026] Generate initial path module, which is used to obtain map data of offline navigation on the mobile terminal and generate initial planning path according to the user's starting location and destination location;

[0027] An optimization module, used to set an optimization model for path planning, find out the optimal planned path, compare the initial planned path with the optimal planned path, and when the difference between the initial planned path and the optimal planned path exceeds a preset threshold, use the optimal planned path as the final planned path;

[0028] The navigation module is used to push the final planned path to the user, and the user navigates through the final planned path.

[0029] Furthermore, the cost function of the planned path includes:

[0030]

[0031] Among them, F(P) is the cost index of the planned path P, k is the number of nodes on the planned path P, α is the first adjustment factor of the cost function, and d(n i , n i+1 ) is the i-th node n on the planned path P i The distance to the i+1th node, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the planned path P from the i-th node n i The estimated travel time of the vehicle to the i+1th node, γ is the third adjustment factor of the cost function, S i is the maximum speed limit of the road where the i-th node is located, δ is the fourth adjustment factor of the cost function, σi is the visibility of the road where the i-th node is located, ∈ is the fifth adjustment factor of the cost function, g(T i ) is the traffic flow T of the road where the i-th node is located i The influence function on the cost function.

[0032] Furthermore, the traffic flow T of the road where the i-th node is located i The influence function g(T i )include:

[0033]

[0034] Where μ is the influence function g(T i ) is the first adjustment factor, v is the influence function g(T i ) is the second adjustment factor, v′ is the influence function g(T i ), τ is the threshold of vehicle flow.

[0035] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0036] Through the above technical solution, the present invention can plan the optimal path for the user based on the data before the navigation is offline, thereby greatly improving the accuracy of the planned path during offline navigation, improving the user experience, and increasing the traffic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0038] Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0039] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0040] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0041] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0042] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0043] The display screen is used to display the user interface of each application.

[0044] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0045] Example 1

[0046] like Figure 1 As shown, an embodiment of the present invention proposes a multi-scenario path planning method based on mobile terminal offline navigation, including:

[0047] Step 101, obtaining map data of offline navigation on the mobile terminal, and generating an initial planning path according to the user's starting location and destination location;

[0048] Step 102, setting an optimization model for path planning, finding an optimal planned path, comparing the initial planned path with the optimal planned path, and when the difference between the initial planned path and the optimal planned path exceeds a preset threshold, taking the optimal planned path as the final planned path;

[0049] Specifically, the cost function of the planned path can quantify the advantages and disadvantages of different paths by modeling the characteristics of each node and edge on the path, thereby providing a basis for path selection, including:

[0050]

[0051] Among them, F(P) is the cost index of the planned path P, k is the number of nodes on the planned path P, α is the first adjustment factor of the cost function, and d(n i , n i+1 ) is the i-th node n on the planned path P i The distance to the i+1th node, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the planned path P from the i-th node n i The estimated travel time of the vehicle to the i+1th node, γ is the third adjustment factor of the cost function, S iis the maximum speed limit of the road where the i-th node is located, δ is the fourth adjustment factor of the cost function, σ i is the visibility of the road where the i-th node is located, ∈ is the fifth adjustment factor of the cost function, g(T i ) is the traffic flow T of the road where the i-th node is located i The influence function on the cost function.

[0052] Specifically, the traffic flow T of the road where the i-th node is located i The influence function g(T i )include:

[0053]

[0054] Where μ is the influence function g(T i ) is the first adjustment factor, v is the influence function g(T i ) is the second adjustment factor, ν′ is the influence function g(T i ), τ is the threshold of vehicle flow.

[0055] Specifically, the constraint functions of the planning path P include:

[0056]

[0057] Among them, G(P) is the constraint index of the planned path P, h(n i , S i ) is the node accessibility evaluation function, which is used to evaluate the maximum speed limit of S i The i-th node n on the path i The accessibility, η i For the i-th node n i The first weight of traffic flow, ζ i For the i-th node n i is the second weight of the traffic flow, θ′ is the adjustment factor of the constraint function, and C is the threshold of the constraint index.

[0058] Specifically, the path planning dynamic adjustment function includes:

[0059]

[0060] Among them, μ′ i For the i-th node n i is the priority weight of , and ξ is the adjustment factor of the path planning dynamic adjustment function.

[0061] Specifically, the node accessibility evaluation function h(n i , S i )include:

[0062]

[0063] Among them, α″ is the adjustment factor of the node accessibility evaluation function, F actual (S i ) is the maximum speed limit S i The current travel time of the road, F max (S i ) is the maximum speed limit S i The maximum travel time of the road, d terrain (S i ) is the maximum speed limit S i The slope of the road.

[0064] Specifically, the optimization model of the path planning includes:

[0065] min(F(P)+D(P)stG(P)≤C).

[0066] Step 103: Push the final planned path to the user, and the user navigates through the final planned path.

[0067] Example 2

[0068] like Figure 2 As shown, an embodiment of the present invention further provides a multi-scenario path planning system based on mobile terminal offline navigation, comprising:

[0069] Generate initial path module, which is used to obtain map data of offline navigation on the mobile terminal and generate initial planning path according to the user's starting location and destination location;

[0070] An optimization module, used to set an optimization model for path planning, find out the optimal planned path, compare the initial planned path with the optimal planned path, and when the difference between the initial planned path and the optimal planned path exceeds a preset threshold, use the optimal planned path as the final planned path;

[0071] Specifically, the cost function of the planned path can quantify the advantages and disadvantages of different paths by modeling the characteristics of each node and edge on the path, thereby providing a basis for path selection, including:

[0072]

[0073] Among them, F(P) is the cost index of the planned path P, k is the number of nodes on the planned path P, α is the first adjustment factor of the cost function, and d(n i , n i+1 ) is the i-th node n on the planned path P i The distance to the i+1th node, β is the second adjustment factor of the cost function, t(ni , n i+1 ) is the planned path P from the i-th node n i The estimated travel time of the vehicle to the i+1th node, γ is the third adjustment factor of the cost function, S i is the maximum speed limit of the road where the i-th node is located, δ is the fourth adjustment factor of the cost function, σ i is the visibility of the road where the i-th node is located, ∈ is the fifth adjustment factor of the cost function, g(T i ) is the traffic flow T of the road where the i-th node is located i The influence function on the cost function.

[0074] Specifically, the traffic flow T of the road where the i-th node is located i The influence function g(T i )include:

[0075]

[0076] Where μ is the influence function g(T i ) is the first adjustment factor, v is the influence function g(T i ) is the second adjustment factor, ν′ is the influence function g(T i ), τ is the threshold of vehicle flow.

[0077] Specifically, the constraint functions of the planning path P include:

[0078]

[0079] Among them, G(P) is the constraint index of the planned path P, h(n i , S i ) is the node accessibility evaluation function, which is used to evaluate the maximum speed limit of S i The i-th node n on the path i The accessibility, η i For the i-th node n i The first weight of traffic flow, ζ i For the i-th node n i is the second weight of the traffic flow, θ′ is the adjustment factor of the constraint function, and C is the threshold of the constraint index.

[0080] Specifically, the path planning dynamic adjustment function includes:

[0081]

[0082] Among them, μ′ i For the i-th node n i is the priority weight of , and ξ is the adjustment factor of the path planning dynamic adjustment function.

[0083] Specifically, the node accessibility evaluation function h(n i , S i )include:

[0084]

[0085] Among them, α″ is the adjustment factor of the node accessibility evaluation function, F actual (S i ) is the maximum speed limit S i The current travel time of the road, F max (S i ) is the maximum speed limit S i The maximum travel time of the road, d terrain (S i ) is the maximum speed limit S i The slope of the road.

[0086] Specifically, the optimization model of the path planning includes:

[0087] min(F(P)+D(P)stG(P)≤C).

[0088] The navigation module is used to push the final planned path to the user, and the user navigates through the final planned path.

[0089] Example 3

[0090] An embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the multi-scenario path planning method based on mobile terminal offline navigation.

[0091] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0092] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining map data for offline navigation of the mobile terminal, and generating an initial planned path according to the user's starting position and destination position;

[0093] Step 102, setting an optimization model for path planning, finding an optimal planned path, comparing the initial planned path with the optimal planned path, and when the difference between the initial planned path and the optimal planned path exceeds a preset threshold, taking the optimal planned path as the final planned path;

[0094] Specifically, the cost function of the planned path can quantify the advantages and disadvantages of different paths by modeling the characteristics of each node and edge on the path, thereby providing a basis for path selection, including:

[0095]

[0096] Among them, F(P) is the cost index of the planned path P, k is the number of nodes on the planned path P, α is the first adjustment factor of the cost function, and d(n i , n i+1 ) is the i-th node n on the planned path P i The distance to the i+1th node, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the planned path P from the i-th node n i The estimated travel time of the vehicle to the i+1th node, γ is the third adjustment factor of the cost function, S i is the maximum speed limit of the road where the i-th node is located, δ is the fourth adjustment factor of the cost function, σ i is the visibility of the road where the i-th node is located, ∈ is the fifth adjustment factor of the cost function, g(T i ) is the traffic flow T of the road where the i-th node is located i The influence function on the cost function.

[0097] Specifically, the traffic flow T of the road where the i-th node is located i The influence function g(T i )include:

[0098]

[0099] Where μ is the influence function g(T i ) is the first adjustment factor, v is the influence function g(T i ) is the second adjustment factor, v′ is the influence function g(T i ), τ is the threshold of vehicle flow.

[0100] Specifically, the constraint functions of the planning path P include:

[0101]

[0102] Among them, G(P) is the constraint index of the planned path P, h(n i , S i ) is the node accessibility evaluation function, which is used to evaluate the maximum speed limit of S i The i-th node n on the path i The accessibility, η i For the i-th node ni The first weight of traffic flow, ζ i For the i-th node n i is the second weight of the traffic flow, θ′ is the adjustment factor of the constraint function, and C is the threshold of the constraint index.

[0103] Specifically, the path planning dynamic adjustment function includes:

[0104]

[0105] Among them, μ′ i For the i-th node n i is the priority weight of , and ξ is the adjustment factor of the path planning dynamic adjustment function.

[0106] Specifically, the node accessibility evaluation function h(n i , S i )include:

[0107]

[0108] Among them, α″ is the adjustment factor of the node accessibility evaluation function, F actual (S i ) is the maximum speed limit S i The current travel time of the road, F max (S i ) is the maximum speed limit S i The maximum travel time of the road, d terrain (S i ) is the maximum speed limit S i The slope of the road.

[0109] Specifically, the optimization model of the path planning includes:

[0110] min(F(P)+D(P)stG(P)≤C).

[0111] Step 103: Push the final planned path to the user, and the user navigates through the final planned path.

[0112] Example 4

[0113] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the multi-scenario path planning method based on mobile offline navigation.

[0114] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0115] Among them, the storage medium can be used to store software programs and modules, such as a multi-scenario path planning method based on offline navigation of a mobile terminal in an embodiment of the present invention, and the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned multi-scenario path planning method based on offline navigation of a mobile terminal. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0116] The processor may call the information and application stored in the storage medium through the transmission system to perform the following steps: Step 101, obtaining map data for offline navigation of the mobile terminal, and generating an initial planning path according to the user's starting position and destination position;

[0117] Step 102, setting an optimization model for path planning, finding an optimal planned path, comparing the initial planned path with the optimal planned path, and when the difference between the initial planned path and the optimal planned path exceeds a preset threshold, taking the optimal planned path as the final planned path;

[0118] Specifically, the cost function of the planned path can quantify the advantages and disadvantages of different paths by modeling the characteristics of each node and edge on the path, thereby providing a basis for path selection, including:

[0119]

[0120] Among them, F(P) is the cost index of the planned path P, k is the number of nodes on the planned path P, α is the first adjustment factor of the cost function, and d(n i , n i+1 ) is the i-th node n on the planned path P i The distance to the i+1th node, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the planned path P from the i-th node n i The estimated travel time of the vehicle to the i+1th node, γ is the third adjustment factor of the cost function, S i is the maximum speed limit of the road where the i-th node is located, δ is the fourth adjustment factor of the cost function, σ iis the visibility of the road where the i-th node is located, ∈ is the fifth adjustment factor of the cost function, g(T i ) is the traffic flow T of the road where the i-th node is located i The influence function on the cost function.

[0121] Specifically, the traffic flow T of the road where the i-th node is located i The influence function g(T i )include:

[0122]

[0123] Where μ is the influence function g(T i ) is the first adjustment factor, v is the influence function g(T i ) is the second adjustment factor, v′ is the influence function g(T i ), τ is the threshold of vehicle flow.

[0124] Specifically, the constraint functions of the planning path P include:

[0125]

[0126] Among them, G(P) is the constraint index of the planned path P, h(n i , S i ) is the node accessibility evaluation function, which is used to evaluate the maximum speed limit of S i The i-th node n on the path i The accessibility, η i For the i-th node n i The first weight of traffic flow, ζ i For the i-th node n i is the second weight of the traffic flow, θ′ is the adjustment factor of the constraint function, and C is the threshold of the constraint index.

[0127] Specifically, the path planning dynamic adjustment function includes:

[0128]

[0129] Among them, μ′ i For the i-th node n i is the priority weight of , and ξ is the adjustment factor of the path planning dynamic adjustment function.

[0130] Specifically, the node accessibility evaluation function h(n i , S i )include:

[0131]

[0132] Among them, α″ is the adjustment factor of the node accessibility evaluation function, F actual (S i ) is the maximum speed limit S i The current travel time of the road, F max (S i ) is the maximum speed limit S i The maximum travel time of the road, d terrain (S i ) is the maximum speed limit S i The slope of the road.

[0133] Specifically, the optimization model of the path planning includes:

[0134] min(F(P)+D(P)stG(P)≤C).

[0135] Step 103: Push the final planned path to the user, and the user navigates through the final planned path.

[0136] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0137] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0141] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0142] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A multi-scenario path planning method based on mobile terminal offline navigation, characterized in that: include: Obtain the map data of the mobile offline navigation and generate the initial planning path according to the user's starting location and destination location; Setting an optimization model for path planning, finding an optimal planned path, comparing the initial planned path with the optimal planned path, and when the difference between the initial planned path and the optimal planned path exceeds a preset threshold, taking the optimal planned path as the final planned path; The final planned path is pushed to the user, and the user navigates through the final planned path.

2. A multi-scenario path planning method based on mobile terminal offline navigation as claimed in claim 1, characterized in that: The cost function for planning the path includes: Among them, F(P) is the cost index of the planned path P, k is the number of nodes on the planned path P, α is the first adjustment factor of the cost function, and d(n i , n i+1 ) is the i-th node n on the planned path P i The distance to the i+1th node, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the planned path P from the i-th node n i The estimated travel time of the vehicle to the i+1th node, γ is the third adjustment factor of the cost function, S i is the maximum speed limit of the road where the i-th node is located, δ is the fourth adjustment factor of the cost function, σ i is the visibility of the road where the i-th node is located, ∈ is the fifth adjustment factor of the cost function, g(T i ) is the traffic flow T of the road where the i-th node is located i The influence function on the cost function.

3. A multi-scenario path planning method based on mobile terminal offline navigation as claimed in claim 2, characterized in that: The traffic flow of the road where the i-th node is located is T i The influence function g(T i )include: Where μ is the influence function g(T i ) is the first adjustment factor, v is the influence function g(T i ) is the second adjustment factor, v′ is the influence function g(T i ), τ is the threshold of vehicle flow.

4. A multi-scenario path planning method based on mobile terminal offline navigation as claimed in claim 3, characterized in that: The constraint functions for planning path P include: Among them, G(P) is the constraint index of the planned path P, h(n i , S i ) is the node accessibility evaluation function, which is used to evaluate the maximum speed limit. S The i-th node n on the path of i i The accessibility, η i For the i-th node n i The first weight of traffic flow, ζ i For the i-th node n i is the second weight of the traffic flow, θ′ is the adjustment factor of the constraint function, and C is the threshold of the constraint index.

5. A multi-scenario path planning method based on mobile terminal offline navigation as claimed in claim 4, characterized in that: The path planning dynamic adjustment functions include: Among them, μ′ i For the i-th node n i is the priority weight of , and ξ is the adjustment factor of the path planning dynamic adjustment function.

6. A multi-scenario path planning method based on mobile terminal offline navigation as claimed in claim 5, characterized in that: Node accessibility evaluation function h(n i , S i )include: Among them, α″ is the adjustment factor of the node accessibility evaluation function, F actual (S i ) is the maximum speed limit S i The current travel time of the road, F max (S i ) is the maximum speed limit S i The maximum travel time of the road, d terrain (S i ) is the maximum speed limit S i The slope of the road.

7. A multi-scenario path planning method based on mobile terminal offline navigation as claimed in claim 6, characterized in that: The optimization model of the path planning includes: min(F(P)+D(P) stG(P)≤C).

8. A multi-scenario path planning system based on mobile offline navigation, characterized in that: include: Generate initial path module, which is used to obtain map data of offline navigation on the mobile terminal and generate initial planning path according to the user's starting location and destination location; An optimization module, used to set an optimization model for path planning, find out the optimal planned path, compare the initial planned path with the optimal planned path, and when the difference between the initial planned path and the optimal planned path exceeds a preset threshold, use the optimal planned path as the final planned path; The navigation module is used to push the final planned path to the user, and the user navigates through the final planned path.

9. A multi-scenario path planning system based on mobile terminal offline navigation as claimed in claim 8, characterized in that: The cost function for planning the path includes: Among them, F(P) is the cost index of the planned path P, k is the number of nodes on the planned path P, α is the first adjustment factor of the cost function, and d(n i , n i+1 ) is the i-th node n on the planned path P i The distance to the i+1th node, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the planned path P from the i-th node n i The estimated travel time of the vehicle to the i+1th node, γ is the third adjustment factor of the cost function, S i is the maximum speed limit of the road where the i-th node is located, δ is the fourth adjustment factor of the cost function, σ i is the visibility of the road where the i-th node is located, ∈ is the fifth adjustment factor of the cost function, g(T i ) is the traffic flow T of the road where the i-th node is located i The influence function on the cost function.

10. A multi-scenario path planning system based on mobile terminal offline navigation as claimed in claim 9, characterized in that: The traffic flow of the road where the i-th node is located is T i The influence function g(T i )include: Where μ is the influence function g(T i ) is the first adjustment factor, v is the influence function g(T i ) is the second adjustment factor, v′ is the influence function g(T i ), τ is the threshold of vehicle flow.

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