Traffic Road Network Planning Method and System Based on Multi-Source Information Fusion

Abstract

The present invention belongs to the field of traffic road network planning, specifically a traffic road network planning method and system based on multi-source information fusion. In the method, in the set of traffic key points, a road network planning area is determined based on the starting point coordinates, passing point coordinates, and ending point coordinates; combining each traffic key point in the road network planning area, a fitness function for traffic road network path planning is created; an improved particle swarm optimization algorithm for the traffic road network is constructed, and based on the created fitness function, the improved particle swarm optimization algorithm is used to optimize the planned path of the traffic road network to obtain the optimal traffic road network planning path and output it. According to the created fitness function, the present invention uses the constructed improved particle swarm optimization algorithm to optimize the planned path of the traffic road network, and can obtain the optimal road network planning path in the target geographical space, making the traffic road network planning more targeted and scientific.

Description

Technical Field

[0001] The present invention belongs to the field of traffic road network planning, and particularly relates to a traffic road network planning method and system based on multi-source information fusion. Background Art

[0002] Traffic is the artery of the economy and the bond of civilization. Traffic infrastructure is a basic, leading, strategic industry and an important service industry. A road is an engineering facility for various vehicles and pedestrians to pass through. Among them, a highway is a road that connects cities and villages and is mainly for automobiles to travel on, with certain technical conditions and facilities; an urban road is a road within the city range for vehicles and pedestrians to pass through, with certain technical conditions and facilities; the boundary between urban roads and highways is generally the boundary line of the urban range. Traffic road network planning is a complex and systematic process aimed at ensuring the effective layout and coordinated development of traffic facilities to meet the needs of social economy and residents' travel.

[0003] In the related art, a common road planning method is to statistically analyze the peak traffic flow and road congestion of each road in real time, and conduct mining and analysis based on the peak traffic flow and road congestion to evaluate whether the current road can meet people's traffic needs, so as to carry out corresponding road planning according to the analysis and evaluation results. However, road planning based on peak traffic flow and road congestion is a lagging operation, often planning and constructing after the road is congested, and it is difficult to meet the actual needs of users. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a traffic road network planning method and system based on multi-source information fusion, aiming to solve the technical problems proposed in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions.

[0006] An embodiment of the present invention provides a traffic road network planning method based on multi-source information fusion, including:

[0007] In response to a traffic road network path planning request for a target geographical space, fuse and process the terrain data of the target geographical space and multi-source data sets affecting road network planning to obtain a set of traffic key points containing characteristic data of influencing factors;

[0008] Based on the starting point coordinates, waypoint coordinates, and ending point coordinates in the set of traffic key points, determine the road network planning area; combine each traffic key point in the road network planning area to create a fitness function for traffic road network path planning;

[0009] Construct an improved particle swarm optimization algorithm for the traffic road network. According to the created fitness function, use the constructed improved particle swarm optimization algorithm to optimize the planned path of the traffic road network, and obtain and output the optimal planned path of the traffic road network.

[0010] Further, the step of fusing the terrain data of the target geographical space and the multi-source data sets affecting the road network planning includes:

[0011] Based on the satellite remote sensing data of the target geographical space, obtain the terrain data of the target geographical space;

[0012] Mark traffic key points according to the terrain data of the target geographical space to obtain multiple traffic key points;

[0013] According to each traffic key point, split the multi-source data sets, extract features from the split data sets, and obtain the influencing factor feature data corresponding to the traffic key points;

[0014] Based on multiple traffic key points, perform data matching on multiple influencing factor feature data, and perform data fusion of the same traffic key points according to the data matching results to obtain a set of traffic key points containing the influencing factor feature data.

[0015] Further, the traffic key point coordinates (x, y) in the road network planning area satisfy the following formula:

[0016] min{x1, x2} - c * |x1 - x2| << x << max{x1, x2} + c * |x1 - x2|

[0017] min{y1, y2} - c * |y1 - y2| << y << max{y1, y2} + c * |y1 - y2|

[0018] In the formula, c represents the expansion coefficient, c ∈ [0.35, 0.5]; (x1, y1) represents the starting point coordinates; (x2, y2) represents the ending point coordinates.

[0019] Further, the step of obtaining the optimal planned path of the traffic road network includes:

[0020] Initialize the parameters of the improved particle swarm optimization algorithm. The initialized parameters include traffic key point coordinates, the number of traffic key points, the population size of the particle swarm, the population learning rate, and the number of iterations;

[0021] Calculate the fitness through the fitness function, take the particle with the best fitness as the global optimal point, and the particle with the sub-optimal fitness as the global sub-optimal point,

[0022] Adopt a linearly decreasing rule to change the inertia weight of the improved particle swarm optimization algorithm;

[0023] Generate explosion particles based on the optimal particle explosion strategy, update the velocities of the optimal particles and the explosion particles, re-obtain the positions of all particles based on the updated velocities, calculate the fitness values of all particles after the update, update the global optimal point and the global sub-optimal point, and stop the search until the maximum number of iterations is reached to obtain the optimal path passing through each traffic key point.

[0024] Further, in the step of improving the inertia weight of the particle swarm algorithm by using a linearly decreasing law, the variation formula of the inertia weight is expressed as:

[0025]

[0026] In the formula, ω represents the inertia weight; ω max represents the maximum weight value, with a value of 0.8; ω min represents the minimum weight value, with a value of 0.5; T max represents the maximum number of iterations; t represents the current iteration number of the particle.

[0027] Further, in the step of generating explosion particles based on the optimal particle explosion strategy, the generation method of the explosion particles is expressed as:

[0028]

[0029] In the formula, ex nj represents the j-dimensional variable of the nth generated explosion particle ex n ; Gbest j represents the optimal particle; rand represents a random number between -1 and 1; t represents the current iteration number; b j represents the j-dimensional variable of the historical optimal position of the particle itself.

[0030] Further, in the step of updating the velocities of the optimal particles and the explosion particles:

[0031] The newly generated particles update their velocities according to the following formula:

[0032] The velocity update strategy for traditional particles other than the newly generated particles is expressed as:

[0033] In the formula, c2 represents the learning factor; ω represents the inertia weight; represents the velocity of the particle before the update; G best represents the global optimal position; represents the current position of the particle; rand represents a random number between 0 and 1; represents the velocity of the newly generated explosion particle after the update; represents the velocity of the traditional particle after the update.

[0034] Further, the steps of creating the traffic road network path planning fitness function include:

[0035] Construct an obstacle constraint function F1 expressed as:

[0036]

[0037] In the formula, Q represents the number of obstacles; d n,q represents the distance between the traffic key point in the nth path segment and the qth obstacle; r q represents the radius of the qth obstacle;

[0038] Construct a smooth curvature cost function F2 expressed as:

[0039]

[0040] In the formula, y' i represents the first derivative of y i coordinate x i at the coordinate (x i , y i ), y″ i represents the second derivative of y i coordinate x i at the coordinate (x i , y i ), n i represents the number of path points on the nth path segment;

[0041] The traffic road network path planning fitness function F is expressed as:

[0042] F = min(F1 + F2)

[0043] In the formula, F represents the traffic road network path planning fitness function; F1 represents the obstacle constraint function; F2 represents the smooth curvature cost function.

[0044] Another embodiment of the present invention provides a traffic road network planning system based on multi-source information fusion, including:

[0045] A data fusion module, configured to, in response to a traffic road network path planning request for a target geographical space, fuse and process the terrain data of the target geographical space and a multi-source data set affecting road network planning to obtain a set of traffic key points including influencing factor feature data;

[0046] A model building module, configured to determine a road network planning area based on the starting point coordinates, waypoint coordinates, and ending point coordinates in the set of traffic key points; and create a traffic road network path planning fitness function in combination with each traffic key point in the road network planning area;

[0047] The road network path planning module is used to construct an improved particle swarm optimization algorithm for the traffic road network. According to the created fitness function, the constructed improved particle swarm optimization algorithm is used to optimize the planned path of the traffic road network, and the optimal traffic road network planning path is obtained and output.

[0048] Compared with the prior art, the beneficial effects of the traffic road network planning method and system based on multi-source information fusion of the present invention are as follows:

[0049] First, the present invention combines each traffic key point in the road network planning area to create a traffic road network path planning fitness function. Among them, the obstacle constraint function of the fitness function can ensure that obstacles that cannot be used for road network construction are avoided during path planning, optimizing the road network planning path; the smooth curvature cost function of the fitness function is used to ensure the smoothness of the planned path, reducing or avoiding the situation of sharp turns in the planned road network.

[0050] Second, the present invention constructs an improved particle swarm optimization algorithm for the traffic road network. According to the created fitness function, the constructed improved particle swarm optimization algorithm is used to optimize the planned path of the traffic road network, and the optimal road network planning path can be obtained in the target geographical space, making the traffic road network planning more targeted and scientific, and the road network planning safer, more convenient, efficient, green, and economical. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0052] Figure 1 It is a flowchart of the implementation of the traffic road network planning method based on multi-source information fusion of the present invention;

[0053] Figure 2 It is a sub-flowchart of the traffic road network planning method based on multi-source information fusion of the present invention;

[0054] Figure 3 It is another sub-flowchart of the traffic road network planning method based on multi-source information fusion provided by the embodiments of the present invention;

[0055] Figure 4 It is a structural block diagram of the traffic road network planning system based on multi-source information fusion of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments in the present application belong to the scope of protection of the present application.

[0057] Reference to "embodiment" in this text means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0058] The following describes in detail the specific implementation of the traffic road network planning method and system based on multi-source information fusion of the present invention in conjunction with specific embodiments.

[0059] Please refer to Figure 1 , in a preferred embodiment of the present invention, a traffic road network planning method based on multi-source information fusion is provided, including the following steps:

[0060] S101: In response to a traffic road network path planning request for a target geographical space, fuse the terrain data of the target geographical space and multi-source data sets affecting road network planning to obtain a set of traffic key points containing characteristic data of influencing factors;

[0061] In the embodiments of the present invention, the terrain data of the target geographical space includes digital elevation model (DEM), contour data, slope and aspect data, water system data, residential area and facility data, vegetation cover data, etc.; the multi-source data sets affecting road network planning include existing traffic road network data, ecological sensitive area data such as the distribution of historical sites and scenic spots in the space, and data such as predicted traffic flow that can affect road network planning; through the fusion processing of these data, comprehensive and accurate geographical information support can be provided for traffic road network path planning, ensuring the scientificity and feasibility of the planning.

[0062] As Figure 2 shown, in the embodiments of the present invention, the step of fusing the terrain data of the target geographical space and multi-source data sets affecting road network planning includes:

[0063] S201: Based on the satellite remote sensing data of the target geographical space, obtain the terrain data of the target geographical space, and mark traffic key points according to the terrain data of the target geographical space to obtain a plurality of traffic key points;

[0064] In the process of traffic key point annotation in the embodiments of the present invention, key terrain feature points are determined, and a digital elevation model (DEM) is used to identify high points, low points, and areas with significant slope changes in the terrain. These areas are often places that need special attention in road planning, such as mountaintops, valleys, the tops and bottoms of slopes, etc.; combining the water system information in the terrain data, mark the positions of rivers and lakes, as well as possible bridge or tunnel crossing points. These locations are crucial for ensuring the continuity of the route and reducing the impact on the natural environment; consider the distribution of settlements and facilities: use settlement data to determine the locations of cities, villages, and important facilities (such as schools and hospitals) so as to consider the service scope and traffic convenience when planning the road network.

[0065] S202: According to each traffic key point, split the multi-source data set, extract features from the split data set, and obtain the influencing factor feature data corresponding to the traffic key point;

[0066] Among them, in the embodiments of the present invention, the splitting of the multi-source data set is based on each traffic key point. Exemplarily, if there are a total of N traffic key points, the multi-source data set is divided into N parts, and each part is used to represent the data information corresponding to the traffic key point.

[0067] S203: Based on multiple traffic key points, perform data matching on multiple influencing factor feature data, and perform data fusion of the same traffic key points according to the data matching results to obtain a traffic key point set containing influencing factor feature data; in the process of multi-source data fusion, overlay and analyze the data for traffic key point annotation described above through GIS software to generate a comprehensive traffic key point map, where each key point contains rich attribute information, such as geographical location, relevant terrain features, surrounding environment, etc. Through this annotation scheme that comprehensively considers multiple factors, it can be ensured that the selection of traffic key points is both scientific and practical, providing a solid foundation for subsequent road network planning.

[0068] Further, please continue to refer to Figure 1 , in the embodiments of the present invention, the traffic road network planning method based on multi-source information fusion in the embodiments of the present invention further includes the following steps:

[0069] S102: Based on the traffic key point coordinates of the starting point and the ending point in the traffic key point set, determine the road network planning area; combine each traffic key point in the road network planning area to create a traffic road network path planning fitness function; where the traffic key point coordinates (x, y) in the road network planning area satisfy the following formula:

[0070] min{x1,x2}-c*|x1 - x2| << x << max{x1,x2}+c*|x1 - x2|

[0071] min{y1,y2}-c*|y1 - y2| << y << max{y1,y2}+c*|y1 - y2|

[0072] Wherein, c represents the expansion coefficient, c ∈ [0.35, 0.5]; (x1, y1) represents the starting point coordinates; (x2, y2) represents the ending point coordinates. If the coefficient c is less than 0.35, there may be too little information about traffic key points in the road network planning area. If the coefficient c is greater than 0.5, there may be too much information about traffic key points in the road network planning area. More information will not be considered during actual path planning.

[0073] The traffic road network path planning fitness function provided by the embodiment of the present invention includes an obstacle constraint function and a smooth curvature cost function. Among them, the obstacle constraint refers to ecological sensitive area data such as ancient scenic spots, national parks, and nature reserves, as well as objects such as mountains and residential buildings on the planned path that cannot be used for path planning. By constructing a fitness function including an obstacle constraint function and a smooth curvature cost function, the present invention can obtain a feasible path planning scheme during the process of optimizing the traffic road network path.

[0074] Further, please continue to refer to Figure 1 In the embodiment of the present invention, the traffic road network planning method based on multi-source information fusion of the embodiment of the present invention further includes the following steps:

[0075] S103: Construct an improved particle swarm algorithm for the traffic road network. According to the created fitness function, use the constructed improved particle swarm algorithm to optimize the planned path of the traffic road network, and obtain and output the optimal traffic road network planning path.

[0076] Further, as Figure 3 shown, the step of obtaining the optimal traffic road network planning path includes:

[0077] S301: Initialize the parameters of the improved particle swarm algorithm. The initialized parameters include traffic key point coordinates, the number of traffic key points, the population size of the particle swarm, the population learning rate, and the number of iterations;

[0078] S302: Calculate the fitness through the fitness function. Take the particle with the best fitness as the global optimal point, and the particle with the sub-optimal fitness as the global sub-optimal point; adopt a linearly decreasing rule to change the inertia weight of the improved particle swarm algorithm;

[0079] S303: Generate explosion particles based on the optimal particle explosion strategy, update the velocities of the optimal particle and the explosion particles, re-obtain the positions of all particles based on the updated velocities, calculate the fitness values of all updated particles, update the global optimal point and the global sub-optimal point until the maximum number of iterations is reached, stop the search, and obtain the optimal path traversing each traffic key point.

[0080] Among them, in the traditional particle swarm optimization algorithm, when the global best particle is updated according to the conventional update formula, the particle positions of the next-generation population are generally worse than those of the previous generation. Therefore, in the present invention, a certain number of explosion particles are generated near the optimal particle, enabling the algorithm to better jump out of the local optimum.

[0081] In addition, in the traditional particle swarm optimization algorithm, the inertia weight is a fixed value. If the value is too large, although it can speed up the search speed of the algorithm, it is not conducive to the algorithm to perform a small-range search for particles in the later stage; if the value is too small, although it increases the local search ability, it is not conducive to the search speed of the algorithm. Therefore, the present invention adopts a linearly decreasing law to change and improve the inertia weight of the particle swarm optimization algorithm. In the step of adopting a linearly decreasing law to change and improve the inertia weight of the particle swarm optimization algorithm, the change formula of the inertia weight is expressed as:

[0082]

[0083] In the formula, ω represents the inertia weight; ω max represents the maximum weight value, with a value of 0.8; ω min represents the minimum weight value, with a value of 0.5; T max represents the maximum number of iterations; t represents the current iteration number of the particle.

[0084] By introducing a linear dynamic inertia weight adjustment method in the embodiments of the present invention, the global search ability and local exploration ability of the particle swarm optimization algorithm are increased, effectively improving the convergence speed and search accuracy of the algorithm.

[0085] Furthermore, in the step of generating explosion particles based on the optimal particle explosion strategy, the explosion particle generation method is expressed as:

[0086]

[0087] In the formula, ex nj represents the j-dimensional variable of the nth generated explosion particle ex n ; Gbest j represents the optimal particle; rand represents a random number between -1 and 1; t represents the current iteration number; b j represents the j-dimensional variable of the historical best position of the particle itself.

[0088] Furthermore, in the step of updating the velocities of the optimal particle and the explosion particles:

[0089] The newly generated particles update their velocities according to the following formula:

[0090] The velocity update strategy for traditional particles other than the newly generated particles is expressed as:

[0091] In the formula, c2 represents the learning factor; ω represents the inertia weight; represents the particle velocity before update; G best represents the global optimal position; represents the current position of the particle; rand represents a random number between 0 and 1; represents the velocity of the newly generated particle after explosion update; represents the velocity of the traditional particle after update.

[0092] Furthermore, the steps of creating the traffic road network path planning fitness function include:

[0093] Construct the obstacle constraint function F1 expressed as:

[0094]

[0095] In the formula, Q represents the number of obstacles; d n,q represents the distance between the traffic key point in the nth path segment and the qth obstacle; r q represents the radius of the qth obstacle;

[0096] Construct the smooth curvature cost function F2 expressed as:

[0097]

[0098] In the formula, y' i represents y i coordinate x i at the coordinate (x i , y i ) of the first derivative, y″ i represents y i coordinate x i at the coordinate (x i , y i ) of the second derivative, n i represents the number of path points on the nth path segment;

[0099] The traffic road network path planning fitness function F is expressed as:

[0100] F = min(F1 + F2)

[0101] In the formula, F represents the traffic road network path planning fitness function; F1 represents the obstacle constraint function; F2 represents the smooth curvature cost function.

[0102] The present invention optimizes the planned path of a traffic road network by constructing an improved particle swarm optimization algorithm for the traffic road network, and based on the created fitness function, using the constructed improved particle swarm optimization algorithm to optimize the planned path of the traffic road network, so as to obtain the optimal road network planning path in the target geographical space, making the traffic road network planning more targeted and scientific, and the road network planning safer, more convenient, efficient, green and economical.

[0103] Further, as Figure 4 shown, another embodiment of the present invention provides a traffic road network planning system based on multi-source information fusion, including:

[0104] A data fusion module 401, configured to, in response to a traffic road network path planning request for a target geographical space, fuse and process the terrain data of the target geographical space and a multi-source data set affecting road network planning to obtain a set of traffic key points including influencing factor feature data;

[0105] A model building module 402, configured to determine a road network planning area based on the starting point coordinates, waypoint coordinates, and ending point coordinates in the set of traffic key points; combine each traffic key point in the road network planning area to create a traffic road network path planning fitness function;

[0106] A road network path planning module 403, configured to construct an improved particle swarm optimization algorithm for the traffic road network, and based on the created fitness function, use the constructed improved particle swarm optimization algorithm to optimize the planned path of the traffic road network to obtain the optimal traffic road network planning path and output it.

[0107] In summary, the present invention combines each traffic key point in the road network planning area to create a traffic road network path planning fitness function. Among them, the obstacle constraint function of the fitness function can ensure that obstacles where road network construction cannot be carried out are avoided during path planning, optimizing the road network planning path; the smooth curvature cost function of the fitness function is used to ensure the smoothness of the planned path and reduce or avoid the situation of sharp turns in the planned road network.

[0108] It should be noted that the device embodiments described above are merely illustrative. 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 to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative work.

[0109] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the above-described device can refer to the corresponding process in the foregoing method embodiment, and will not be elaborated herein.

[0110] Another preferred embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the traffic road network planning based on multi-source information fusion as described in any one of the above embodiments.

[0111] The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0112] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device, and connects various parts of the entire terminal device through various interfaces and lines.

[0113] The memory can be used to store the computer program. The processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0114] Another preferred embodiment of the present invention provides a storage medium, which is a computer-readable storage medium. The computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented.

[0115] Wherein, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0116] The above is the preferred implementation manner of the present invention. It should be noted that for those of ordinary skill in the art of the present technology, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A traffic road network planning method based on multi-source information fusion, characterized in that, Including: In response to a traffic road network path planning request for a target geospatial area, fuse the terrain data of the target geospatial area and multi-source data sets affecting road network planning to obtain a set of traffic key points containing influencing factor feature data; Based on the starting point coordinates, waypoint coordinates, and ending point coordinates in the set of traffic key points, determine the road network planning area; Combine each traffic key point in the road network planning area to create a traffic road network path planning fitness function; The steps of creating a traffic road network path planning fitness function include: Construct an obstacle constraint function F1 expressed as: where Q represents the number of obstacles; d n,q represents the distance between the traffic key point in the nth path segment and the qth obstacle; r q represents the radius of the qth obstacle; Construct a smooth curvature cost function F2 expressed as: where y' i is expressed as y i coordinate x i at coordinate (x i , y i ), the first derivative, y″ i is expressed as y i coordinate x i at coordinate (x i , y i ), the second derivative, n i represents the number of path points on the n-th path segment; The traffic road network path planning fitness function F is expressed as: F = min(F1 + F2) In the formula, F represents the traffic road network path planning fitness function; F1 represents the obstacle constraint function; F2 represents the smooth curvature cost function; Construct an improved particle swarm algorithm for the traffic road network. According to the created fitness function, use the constructed improved particle swarm algorithm to optimize the planned path of the traffic road network, and obtain and output the optimal traffic road network planning path; The steps of obtaining the optimal traffic road network planning path include: Initialize the parameters of the improved particle swarm algorithm. The initialized parameters include traffic key point coordinates, the number of traffic key points, the population size of the particle swarm, the population learning rate, and the number of iterations; Calculate the fitness through the fitness function. Take the particle with the best fitness as the global optimal point, and the particle with the sub-optimal fitness as the global sub-optimal point. Adopt a linearly decreasing rule to change the inertia weight of the improved particle swarm algorithm; Generate explosion particles based on the optimal particle explosion strategy, update the velocities of the optimal particle and the explosion particles, re-obtain the positions of all particles based on the updated velocities, calculate the fitness values of all updated particles, and update the global optimal point and the global sub-optimal point until the maximum number of iterations is reached, stop the search, and obtain the optimal path traversing each traffic key point.

2. The traffic road network planning method based on multi-source information fusion according to claim 1, characterized in that The steps of fusing the terrain data of the target geospatial area and multi-source data sets affecting road network planning include: Based on the satellite remote sensing data of the target geospatial area, obtain the terrain data of the target geospatial area; Perform traffic key point annotation according to the terrain data of the target geospatial area to obtain multiple traffic key points; According to each traffic key point, split the multi-source data set, extract features from the split data set, and obtain the influencing factor feature data corresponding to the traffic key point; Based on multiple traffic key points, perform data matching on multiple influencing factor feature data, and perform data fusion of the same traffic key points according to the data matching results to obtain a set of traffic key points containing influencing factor feature data.

3. The traffic road network planning method based on multi-source information fusion according to claim 2, wherein The traffic key point coordinates (x, y) in the road network planning area satisfy the following formula: min{x1, x2} - c * |x1 - x2| << x << max{x1, x2} + c * |x1 - x2| min{y1, y2} - c * |y1 - y2| << y << max{y1, y2} + c * |y1 - y2| In the formula, c represents the expansion coefficient, c ∈ [0.35, 0.5]; (x1, y1) represents the starting point coordinates; (x2, y2) represents the ending point coordinates.

4. The traffic road network planning method based on multi-source information fusion according to claim 3, characterized in that In the step of improving the inertia weight of the particle swarm optimization algorithm by using a linearly decreasing law, the variation formula of the inertia weight is expressed as: In the formula, ω represents the inertia weight; ω max represents the maximum weight value, with a value of 0.8; ω min represents the minimum weight value, with a value of 0.5; T max represents the maximum number of iterations; t represents the current iteration number of the particle.

5. The traffic road network planning method based on multi-source information fusion according to claim 4, characterized in that In the step of generating explosion particles based on the optimal particle explosion strategy, the generation method of the explosion particles is expressed as: where, $e_{x}$ nj represents the $j$-dimensional variable of the $n$-th generated explosion particle $e_{x}$ n ; $G_{best}$ j represents the optimal particle; $rand$ represents a random number between -1 and 1; $t$ represents the current iteration number; $b$ j represents the $j$-dimensional variable of the historical best position of the particle itself.

6. The traffic road network planning method based on multi-source information fusion according to claim 5, characterized in that In the step of updating the velocities of the optimal particle and the explosion particle: The velocity of the newly generated particles is updated according to the following formula: The update speed strategy of traditional particles other than newly generated particles is expressed as: Wherein, c2 represents the learning factor; ω represents the inertia weight; represents the particle velocity before update; G best represents the global optimal position; represents the current position of the particle; rand represents a random number between 0 and 1; represents the velocity of the newly generated particle after explosion update; represents the velocity of the traditional particle after update.

7. A system for implementing the traffic road network planning method based on multi-source information fusion according to any one of claims 1-6, characterized in that The system includes: A data fusion module, configured to, in response to a traffic road network path planning request for a target geographical space, fuse the terrain data of the target geographical space and a multi-source data set affecting road network planning to obtain a traffic key point set including influence factor feature data; A model building module, configured to determine a road network planning area based on the starting point coordinates, waypoint coordinates, and ending point coordinates in the traffic key point set; combine each traffic key point in the road network planning area to create a traffic road network path planning fitness function; A road network path planning module, configured to construct an improved particle swarm optimization algorithm for the traffic road network, and optimize the planned path of the traffic road network by using the constructed improved particle swarm optimization algorithm according to the created fitness function to obtain and output an optimal traffic road network planning path.

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