A multi-scenario path planning method and system based on mobile offline navigation

Abstract

The present invention discloses a multi-scenario path planning method and system based on mobile offline navigation. The method includes: obtaining map data of mobile offline navigation, and generating an initial planned path according to the starting position and destination position of the user; setting an optimization model for path planning, finding the 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; pushing the final planned path to the user, and the user performs navigation through the final planned path.

Description

Technical Field

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

[0002] Mobile offline navigation technology mainly uses a built-in processed domain-based navigation electronic map to enable a mobile device to use an external navigation locator to achieve accurate and continuous navigation services without Internet support.

[0003] However, in the prior art, since the navigation is in an offline state, there will be deviations in path planning, resulting in the problem of being unable to determine the optimal path. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a multi-scenario path planning method based on mobile offline navigation, including:

[0005] Obtain the map data of mobile offline navigation, and generate an initial planned path according to the starting position and destination position of the user;

[0006] Set an optimization model for path planning, find 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;

[0007] Push the final planned path to the user, and the user navigates through the final planned path.

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

[0009]

[0010] Wherein, 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, d(n i , n i+1 ) is the distance from the i-th node n i to the (i + 1)-th node on the planned path P, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the estimated driving time of the vehicle from the i-th node n i to the (i + 1)-th node on the planned path P, 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, and g(T i ) is the traffic flow of the road where the i-th node is located, T i is the influence function on the cost function.

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

[0012]

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

[0014] Furthermore, the constraint function of the planned path P includes:

[0015]

[0016] where G(P) is the constraint index of the planned path P, h(n i , S i ) is the node passability evaluation function, which is used to evaluate the passability of the i-th node n i on the road with the maximum speed limit of S i , η i is the first weight of the traffic flow of the i-th node n i , ζ i is the second weight of the traffic flow of the i-th node n i , 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] where μ′ i is the priority weight of the i-th node n i , and ξ is the adjustment factor of the path planning dynamic adjustment function.

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

[0021]

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

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

[0024] min(F(P)+D(P) s.t. G(P) ≤ C).

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

[0026] An initial path generation module, configured to obtain map data of mobile offline navigation, and generate an initial planned path according to the starting position and destination position of the user;

[0027] An optimization module, configured to set an optimization model of path planning, find an 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] A navigation module, configured 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] where 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, d(n i , n i+1 ) is the distance from the i-th node n i to the (i + 1)-th node on the planned path P, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the estimated travel time of the vehicle from the i-th node n i to the (i + 1)-th node on the planned path P, 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, and g(T i ) is the traffic flow T on the road where the i-th node is located i influence function on the cost function.

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

[0033]

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

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

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

[0037] Figure 1 is the flowchart of the method according to Embodiment 1 of the present invention;

[0038] Figure 2 is the system structure diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0039] In order to better understand the above technical solution, the above technical solution will be described in detail below in combination with the accompanying drawings of the specification and specific embodiments.

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

[0041] The processor may include one or more processing cores. The processor connects various parts within the entire terminal through various interfaces and lines, 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 by calling data stored in the storage medium.

[0042] The storage medium may include a Random Access Memory (RAM), or may also include a Read-Only Memory (ROM). The storage medium can be used to store instructions, programs, codes, code sets or instructions.

[0043] The display screen is used to display the user interfaces of various application programs.

[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. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, etc., which will not be elaborated here.

[0045] Embodiment 1

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

[0047] Step 101, obtain the map data of mobile offline navigation, and generate an initial planned path according to the starting position and destination position of the user;

[0048] Step 102, set an optimization model for path planning, find 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;

[0049] Specifically, the cost function of the planned path models the characteristics of each node and edge on the path. The cost function can quantify the advantages and disadvantages of different paths, so as to provide a basis for path selection, specifically 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, d(n i , n i+1 ) is the distance from the i-th node n i to the (i + 1)-th node on the planned path P, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the estimated driving time of the vehicle from the i-th node n i to the (i + 1)-th node on the planned path P, γ is the third adjustment factor of the cost function, S iis the speed limit of the road where the $i$-th node is located, $\delta$ is the fourth adjustment factor of the cost function, and $\sigma$ i is the visibility of the road where the $i$-th node is located, $\epsilon$ is the fifth adjustment factor of the cost function, and $g(T$ i ) is the traffic flow $T$ of the road where the $i$-th node is located i influence function on the cost function.

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

[0053]

[0054] where $\mu$ is the first adjustment factor of the influence function $g(T$ i ), $v$ is the second adjustment factor of the influence function $g(T$ i ), $v'$ is the third adjustment factor of the influence function $g(T$ i ), and $\tau$ is the threshold of the traffic flow.

[0055] Specifically, the constraint function of the planned path $P$ includes:

[0056]

[0057] where $G(P)$ is the constraint index of the planned path $P$, and $h(n$ i , $S$ i ) is the node passability evaluation function, which is used to evaluate the passability of the $i$-th node $n$ i on the road with a speed limit of $S$ i . $\eta$ i is the first weight of the traffic flow of the $i$-th node $n$ i , and $\zeta$ i is the second weight of the traffic flow of the $i$-th node $n$ i . $\theta'$ 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] where $\mu'$ i is the priority weight of the $i$-th node $n$ i , and $\xi$ is the adjustment factor of the path planning dynamic adjustment function.

[0061] Specifically, the node passability evaluation function $h(n$ i , $S$ i ) includes:

[0062]

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

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

[0065] min(F(P)+D(P) s.t. G(P)≤C).

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

[0067] Embodiment 2

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

[0069] An initial path generation module, configured to obtain map data of mobile offline navigation, and generate an initial planned path according to the starting position and destination position of the user;

[0070] An optimization module, configured to set an optimization model for path planning, find 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 models the characteristics of each node and edge on the path. The cost function can quantify the advantages and disadvantages of different paths, so as to provide a basis for path selection. Specifically, it includes:

[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, d(n i , n i+1 ) is the distance from the i-th node n i to the (i + 1)-th node on the planned path P, β is the second adjustment factor of the cost function, t(ni , n i+1 ) is the expected driving time of the vehicle from the i-th node n i to the (i + 1)-th node on the planned path P. γ is the third adjustment factor of the cost function, and S i is the speed limit of the road where the i-th node is located. δ is the fourth adjustment factor of the cost function, and σ i is the visibility of the road where the i-th node is located. ∈ is the fifth adjustment factor of the cost function, and g(T i ) is the influence function of the traffic flow T i on the cost function.

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

[0075]

[0076] Among them, μ is the first adjustment factor of the influence function g(T i ), v is the second adjustment factor of the influence function g(T i ), ν′ is the third adjustment factor of the influence function g(T i ), and τ is the threshold of the traffic flow.

[0077] Specifically, the constraint function of the planned path P includes:

[0078]

[0079] Among them, G(P) is the constraint index of the planned path P, and h(n i , S i ) is the node passability evaluation function, which is used to evaluate the passability of the i-th node n i on the road with a speed limit of S i . η i is the first weight of the traffic flow of the i-th node n i , and ζ i is the second weight of the traffic flow of the i-th node n i . 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 is the priority weight of the i-th node n i , and ξ is the adjustment factor of the path planning dynamic adjustment function.

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

[0084]

[0085] where α″ is the adjustment factor of the node passability evaluation function, F actual (S i ) is the current travel time of the road with a maximum speed limit of S i , F max (S i ) is the maximum travel time of the road with a maximum speed limit of S i , and d terrain (S i ) is the slope of the road with a maximum speed limit of S i .

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

[0087] min(F(P)+D(P) s.t. G(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] The embodiment of the present invention also proposes a storage medium storing multiple instructions for implementing the multi-scenario path planning method based on mobile offline navigation.

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

[0092] Optionally, in this embodiment, the storage medium is set to store program codes for performing the following steps: Step 101, obtain the map data of mobile offline navigation, and generate an initial planned path according to the starting position and destination position of the user;

[0093] Step 102, set the optimization model of path planning, find 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;

[0094] Specifically, the cost function of the planned path models the characteristics of each node and edge on the path. The cost function can quantify the advantages and disadvantages of different paths, thereby providing a basis for path selection, specifically 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, d(n i , n i+1 ) is the distance from the i-th node n i to the (i + 1)-th node on the planned path P, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the estimated driving time of the vehicle from the i-th node n i to the (i + 1)-th node on the planned path P, γ is the third adjustment factor of the cost function, S i is the 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 influence function of the traffic flow T i on the cost function.

[0097] Specifically, the influence function g(T i ) of the traffic flow T i on the cost function includes:

[0098]

[0099] Among them, μ is the first adjustment factor of the influence function g(T i ), v is the second adjustment factor of the influence function g(T i ), v′ is the third adjustment factor of the influence function g(T i ), and τ is the traffic flow threshold.

[0100] Specifically, the constraint function of the planned path P includes:

[0101]

[0102] Among them, G(P) is the constraint index of the planned path P, h(n i , S i ) is the node passability evaluation function, which is used to evaluate the passability of the i-th node n i on the road with a speed limit of S i , η i is the i-th node ni The first weight of the traffic flow, ζ i For the i-th node n i 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 The priority weight, ξ is the adjustment factor of the path planning dynamic adjustment function.

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

[0107]

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

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

[0110] min(F(P)+D(P)s.t.G(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] The embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor. 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 described above.

[0114] Specifically, the electronic equipment in this embodiment can be a computer terminal, and the computer terminal can 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 mobile offline navigation in the embodiments of the present invention, and the corresponding program instructions / modules. The processor runs the software programs and modules stored in the storage medium to execute various functional applications and data processing, that is, to implement the above-mentioned multi-scenario path planning method based on mobile offline navigation. The storage medium may include a high-speed random access 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 provided with respect to the processor, and these remote storage media can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0116] The processor can call the information and application programs stored in the storage medium through the transmission system to execute the following steps: Step 101, obtain the map data of mobile offline navigation, and generate an initial planned path according to the starting position and destination position of the user;

[0117] Step 102, set an optimization model for path planning, find 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;

[0118] Specifically, the cost function of the planned path models the characteristics of each node and edge on the path. The cost function can quantify the advantages and disadvantages of different paths, so as to provide a basis for path selection, specifically 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, d(n i , n i+1 ) is the distance from the i-th node n i to the (i + 1)-th node on the planned path P, β is the second adjustment factor of the cost function, t(n i , n i+1 ) is the estimated driving time of the vehicle from the i-th node n i to the (i + 1)-th node on the planned path P, 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 iVisibility 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 of the road where the i-th node is located T i Influence function on the cost function.

[0121] Specifically, the traffic flow T of the road where the i-th node is located i Influence function g(T i ) on the cost function includes:

[0122]

[0123] Among them, μ is the first adjustment factor of the influence function g(T i ), v is the second adjustment factor of the influence function g(T i ), v′ is the third adjustment factor of the influence function g(T i ), and τ is the threshold of traffic flow.

[0124] Specifically, the constraint function of the planned path P includes:

[0125]

[0126] Among them, G(P) is the constraint index of the planned path P, h(n i , S i ) is the node passability evaluation function, which is used to evaluate the passability of the i-th node n i on the road with the maximum speed limit of S i , η i is the first weight of the traffic flow of the i-th node n i , ζ i is the second weight of the traffic flow of the i-th node n i , 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 is the priority weight of the i-th node n i , and ξ is the adjustment factor of the path planning dynamic adjustment function.

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

[0131]

[0132] where α″ is the adjustment factor of the node passability evaluation function, F actual (S i ) is the current travel time of the road with a maximum speed limit of S i , F max (S i ) is the maximum travel time of the road with a maximum speed limit of S i , and d terrain (S i ) is the slope of the road with a maximum speed limit of S i .

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

[0134] min(F(P)+D(P)s.t.G(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 descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may 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 illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. 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 displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces, and the indirect coupling or communication connection of units or modules can be in an electrical or other form.

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

[0140] In addition, in each embodiment of the present invention, each functional unit may be integrated into one processing unit, may exist physically alone for each unit, 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 a software functional unit.

[0141] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, read-only storage media (ROM, Read-Only Memory), random access storage media (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0142] Obviously, the above embodiments are merely examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill 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 enumerate all the implementation manners here. The obvious changes or modifications derived therefrom still fall within the protection scope of the present invention.

Claims

1. A multi-scenario path planning method based on mobile offline navigation, characterized in that, Including: Obtain the map data for mobile offline navigation, and generate an initial planned path according to the user's starting position and destination position; Set an optimization model for path planning, find 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; Among them, the optimization model for path planning includes: a cost function for the planned path, specifically: ， Wherein, is the cost index of the planned path , is the number of nodes on the planned path , is the first adjustment factor of the cost function is the distance from the th node to the th node on the planned path is the second adjustment factor of the cost function is the expected driving time of the vehicle from the th node to the th node on the planned path is the third adjustment factor of the cost function is the speed limit of the road where the th node is located is the fourth adjustment factor of the cost function is the visibility of the road where the th node is located is the fifth adjustment factor of the cost function is the traffic flow of the road where the th node is located influence function on the cost function; The traffic flow of the road where the th node is located influence function on the cost function includes: ， Among them, is the first adjustment factor of the influence function , is the second adjustment factor of the influence function , is the third adjustment factor of the influence function , is the threshold of traffic flow; Push the final planned path to the user, and the user navigates through the final planned path.

2. The multi-scenario path planning method based on mobile offline navigation according to claim 1, characterized in that Planned path The constraint functions include: ， wherein, is the constraint index of the planned path , is the node passability evaluation function, which is used to evaluate the passability of the th node on the road with the maximum speed limit of , is the first weight of the traffic flow of the th node , is the second weight of the traffic flow of the th node , is the adjustment factor of the constraint function is the threshold of the constraint index.

3. A multi-scenario path planning method based on mobile offline navigation according to claim 2, characterized in that, The path planning dynamic adjustment function includes: ， Among them, is the priority weight of the nth node, and is the adjustment factor of the path planning dynamic adjustment function.

4. The multi-scenario path planning method based on mobile offline navigation according to claim 3, wherein Node passability evaluation function including: ， Among them, is the adjustment factor of the node passability evaluation function, is the current travel time of the road with the maximum speed limit of , is the maximum travel time of the road with the maximum speed limit of , is the slope of the road with the maximum speed limit of .

5. The multi-scenario path planning method based on mobile offline navigation according to claim 4, characterized in that The optimization model for path planning includes: 。 6. A multi-scenario path planning system based on mobile offline navigation, characterized in that, Including: An initial path generation module for obtaining the map data for mobile offline navigation and generating an initial planned path according to the user's starting position and destination position; An optimization module for setting an optimization model for path planning, finding the 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, using the optimal planned path as the final planned path; Among them, the optimization model for path planning includes: a cost function for the planned path, specifically: ， Wherein, is the cost index of the planned path , is the number of nodes on the planned path , is the first adjustment factor of the cost function is the distance from the th node to the th node on the planned path is the second adjustment factor of the cost function is the expected driving time of the vehicle from the th node to the th node on the planned path is the third adjustment factor of the cost function is the maximum speed limit of the road where the th node is located is the fourth adjustment factor of the cost function is the visibility of the road where the th node is located is the fifth adjustment factor of the cost function is the traffic flow of the road where the th node is located influence function on the cost function; The traffic flow of the road where the th node is located and the influence function on the cost function include: ， Among them, is the first adjustment factor of the influence function , is the second adjustment factor of the influence function , is the third adjustment factor of the influence function , is the threshold of traffic flow; A navigation module for pushing the final planned path to the user, and the user navigates through the final planned path.

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