Road network topological graph generation method and device, electronic equipment and medium

Abstract

The embodiment of the invention provides a road network topological graph generation method and device, electronic equipment and a medium. The road network topological graph generation method and device have the following beneficial effects that track data are acquired; generating a track grid map according to the track data; obtaining a time sequence track representation of each track according to the track data; obtaining the track grid representation of each clustering category according to the track grid map; determining the clustering category of each track through the similarity between the time sequence track representation of each track and the track grid representation; fusing the trajectory grid representation corresponding to the determined clustering category with the time sequence trajectory representation to obtain a fused representation; and finally generating a corresponding road network topological graph through model training according to the fusion representation. According to the method, the image data represented by the track and the time sequence track data are fused, the representation capability of the road network topological graph is enhanced, the precision is effectively improved, and the generation efficiency is improved through an end-to-end generation method without an additional algorithm.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to a method, device, electronic equipment and medium for generating a road network topology map. Background Art

[0002] The topological structure of the road network plays an important role in electronic navigation and traffic flow monitoring. In particular, the directionality and connectivity of the road network topological structure play a decisive role in the automatic path planning of the intelligent transportation system.

[0003] However, the existing road network topology map needs to be generated through a large number of user driving trajectories, and with the acceleration of urban development, road network information is usually constantly updated. Therefore, the original road network topology map generation method has the problems of being time-consuming and inaccurate.

[0004] Therefore, how to improve the efficiency and accuracy of road network topology map generation is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In order to overcome the shortcomings of low efficiency and poor accuracy of existing road network topology map generation methods, the present invention proposes a road network topology map generation method, device, electronic device and medium.

[0006] In order to achieve the above object, according to a first aspect of the present invention, an embodiment of the present invention provides a method for generating a road network topology map, the method comprising the following steps:

[0007] Get trajectory data;

[0008] Generate a trajectory grid map according to the trajectory data;

[0009] Inputting the trajectory data into the Transformer model to obtain a temporal trajectory representation of each trajectory;

[0010] Input the trajectory grid graph into the VIT model, and obtain the trajectory grid representation of each cluster category represented by the trajectory grid graph through MLP;

[0011] Calculate the similarity between the time series trajectory representation and the trajectory grid representation of each trajectory, and determine the clustering category of each trajectory;

[0012] The trajectory grid representation and the time series trajectory representation corresponding to the determined clustering category are fused to obtain a fused representation of each trajectory;

[0013] New trajectory data is generated through the Transformer model according to the fusion representation, and the corresponding road network topology map is generated through integration through learning of the real grid map corresponding to the real road network map.

[0014] Optionally, generating a trajectory grid map according to the trajectory data includes:

[0015] Splitting the trajectory data into trajectory points of preset length;

[0016] Calculate the similarity of each trajectory point based on the Frechet distance, delete abnormal trajectory points and update the trajectory data;

[0017] Generate a trajectory grid map based on the updated trajectory data.

[0018] Optionally, the clustering category includes a combination of one or more of a straight trajectory category, a left turn trajectory category, and a right turn trajectory analogy.

[0019] Optionally, the step of fusing the trajectory grid representation corresponding to the determined cluster category with the time series trajectory representation to obtain a fused representation of each trajectory includes:

[0020] Add the trajectory grid representation and time series trajectory representation of the same cluster category to obtain the fused representation of each trajectory;

[0021] or,

[0022] The attention module is used to fuse the trajectory grid representation and time series trajectory representation of the same clustering category to obtain the fused representation of each trajectory.

[0023] According to a second aspect of the present invention, an embodiment of the present invention further provides a road network topology map generating device, comprising:

[0024] An acquisition module, used to obtain trajectory data;

[0025] A trajectory grid map generating module, used to generate a trajectory grid map according to the trajectory data;

[0026] A time series trajectory representation module, used for inputting the trajectory data into a Transformer model to obtain a time series trajectory representation of each trajectory;

[0027] A trajectory grid representation module, used for inputting the trajectory grid graph into a VIT model, and obtaining a trajectory grid representation of each cluster category represented by the trajectory grid graph through MLP;

[0028] A clustering module, used to calculate the similarity between the time series trajectory representation and the trajectory grid representation of each trajectory, and determine the clustering category of each trajectory;

[0029] A fusion representation module, used to fuse the trajectory grid representation corresponding to the determined cluster category with the time series trajectory representation to obtain a fusion representation of each trajectory;

[0030] A generation module is used to generate new trajectory data through a Transformer model according to the fusion representation, and integrate and generate a corresponding road network topology map after learning the real grid map corresponding to the real road network map.

[0031] Optionally, the trajectory grid map generating module is further used to:

[0032] Splitting the trajectory data into trajectory points of preset length;

[0033] Calculate the similarity of each trajectory point according to the Frechet distance, delete the trajectory point and update the trajectory data;

[0034] Generate a trajectory grid map based on the updated trajectory data.

[0035] Optionally, the clustering category includes a combination of one or more of a straight trajectory category, a left turn trajectory category, and a right turn trajectory analogy.

[0036] Optionally, the fusion characterization module is further used to:

[0037] Add the trajectory grid representation and time series trajectory representation of the same cluster category to obtain the fused representation of each trajectory;

[0038] or,

[0039] The attention module is used to fuse the trajectory grid representation and time series trajectory representation of the same clustering category to obtain the fused representation of each trajectory.

[0040] According to the third aspect of the present invention, an embodiment of the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the road network topology map generating method in any of the above embodiments are implemented.

[0041] According to the fourth aspect of the present invention, an embodiment of the present invention also provides a storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the steps of the road network topology map generation method in any of the above embodiments.

[0042] As described above, a method, device, electronic device and medium for generating a road network topology map provided by an embodiment of the present invention have the following beneficial effects: obtaining trajectory data; generating a trajectory grid map according to the trajectory data; inputting the trajectory data into a Transformer model to obtain a temporal trajectory representation of each trajectory; inputting the trajectory grid map into a VIT model, and obtaining a trajectory grid representation of each clustering category represented by the trajectory grid map through MLP; determining the clustering category of each trajectory through the similarity between the temporal trajectory representation and the trajectory grid representation of each trajectory; fusing the trajectory grid representation corresponding to the determined clustering category with the temporal trajectory representation to obtain a fused representation of each trajectory; generating new trajectory data through a Transformer model according to the fused representation, learning through a real grid map corresponding to a real road network map, and integrating to generate a corresponding road network topology map. The present invention fuses the image data and the temporal trajectory data represented by the trajectory through a model, thereby enhancing the representation capability of the road network topology map, thereby effectively improving the accuracy, and the generation efficiency is further improved by an end-to-end generation method without the need to add an additional extraction algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of a process of generating a road network topology map provided by an embodiment of the present invention;

[0044] Figure 2 It is a schematic diagram of a flow chart of a method for generating a trajectory grid map provided by an embodiment of the present invention;

[0045] Figure 3 It is a schematic diagram of the road network topology effect provided by an embodiment of the present invention;

[0046] Figure 4 It is a schematic diagram of the structure of a road network topology map generating device provided by an embodiment of the present invention;

[0047] Figure 5 The present invention is a schematic diagram of the hardware structure of an electronic device for executing a method for generating a road network topology map provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0049] See also Figures 1 to 5It should be noted that the illustrations provided in this embodiment are only used to illustrate the basic concept of the present invention in a schematic manner, and the illustrations only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0050] See also Figure 1 , is a schematic diagram of a flow chart of a method for generating a road network topology map provided by an embodiment of the present invention, such as Figure 1 As shown, the embodiment of the present invention shows the process of the method for generating a road network topology map.

[0051] Step S101: Acquire trajectory data.

[0052] In the specific implementation, the trajectory data can be collected by GPS (Global Positioning System) to obtain continuous trajectory points, that is, τ = <(g1, t1), (g2, t2), ..., (g n ,t n )>, for each (g i ,t i ), i is a natural number, g i Represents each longitude and latitude coordinate value, t i Represents the time of recording. The present invention generates a road network topology map of the area covered by the track based on the above track data, that is, G=(V, E), where V is a point of the road network and E is an edge of the road network.

[0053] Step S102: generating a trajectory grid diagram according to the trajectory data.

[0054] The trajectory data obtained in step S101 is used to further generate a trajectory grid map.

[0055] Since there may be noise in the trajectory data, in order to improve the accuracy of the trajectory grid map, see Figure 2 , is a flow chart of a method for generating a trajectory grid map provided by an embodiment of the present invention, such as Figure 2 As shown, the embodiment of the present invention shows the process of the trajectory grid map generation method.

[0056] Step S1021: split the trajectory data into trajectory points of preset length.

[0057] In an embodiment of the present invention, the trajectory points on a road can be split according to a preset length. Specifically, if the road includes 100 data points, the preset length can be 25, so that the data points on the road can be split into 4 parts.

[0058] Step S1022: Calculate the similarity of each trajectory point according to the Frechet distance, delete abnormal trajectory points and update the trajectory data.

[0059] After splitting the trajectory points, the present invention uses the Frechet distance to filter the trajectory points, specifically calculating the similarity of each trajectory point. Further, those trajectory points with poor similarity represent that there is a large degree of abnormal distance with the matched road, and thus are deleted as abnormal trajectory points, and finally the trajectory data is updated, so that the updated trajectory data completes the data filtering to ensure the accuracy of the subsequent trajectory grid map.

[0060] Step S1023: Generate a trajectory grid map according to the updated trajectory data.

[0061] The accurate trajectory data T is screened out, whose shape is [N, L, 2], where N is the number of trajectories and L is each trajectory τ, and the corresponding trajectory grid map is generated according to the trajectory data.

[0062] Step S103: inputting the trajectory data into the Transformer model to obtain a temporal trajectory representation of each trajectory.

[0063] Input the trajectory data into the Transformer model to obtain the temporal trajectory representation T of each trajectory t = Transformer(T). The Transformer model is a deep learning architecture, which is trained by an encoder and a decoder to obtain a trajectory representation, which will not be described in detail in the embodiments of the present invention.

[0064] Step S104: input the trajectory grid graph into the VIT model, and obtain the trajectory grid representation of each cluster category represented by the trajectory grid graph through MLP.

[0065] VIT (Vision Transformer) is a deep learning method for processing image data using the Transformer architecture. In the present invention, the above-mentioned trajectory grid graph is input into the VIT model, and the trajectory grid representation of each clustering category represented by the trajectory grid graph is obtained through MLP (Multilayer Perceptron).

[0066] Imgk=Grid(Tk)

[0067] TG embedding = ViT (Imgk)

[0068] RT embedding=MLP(TG embedding)

[0069] As shown above, the process of obtaining the trajectory grid representation is schematically shown. k represents the grid graph, T k is the trajectory data, ViT represents the VIT model, TG embedding represents the intermediate result after being processed by the VIT model, and this TG embedding result is input into the MLP multi-layer perceptron to finally obtain the trajectory grid representation RT embedding.

[0070] In a specific implementation, the clustering category may include a combination of one or more of a straight trajectory category, a left turn trajectory category, and a right turn trajectory category.

[0071] Step S105: Calculate the similarity between the temporal trajectory representation and the trajectory grid representation of each trajectory, and determine the clustering category of each trajectory.

[0072] After obtaining the temporal trajectory representation of each trajectory through the Transformer encoder, the cluster category to which the trajectory belongs is obtained by calculating the similarity with the road network trajectory representation.

[0073] Step S106: Fusing the trajectory grid representation and the time series trajectory representation corresponding to the determined clustering category to obtain a fused representation of each trajectory.

[0074] In an exemplary embodiment, in order to obtain a fused representation for each trajectory, the trajectory grid representation and the temporal trajectory representation of the same cluster category may be added together to obtain a fused representation for each trajectory.

[0075] In an exemplary embodiment, the attention module in the Transformer can also be used to fuse the trajectory grid representation and the time series trajectory representation of the same clustering category to obtain a fused representation of each trajectory.

[0076]

[0077] The process shown above realizes the optimization process of the fusion representation of the trajectory, where C j is the split trajectory area, T k is the trajectory data in the area, Transformer Encoder is the transformer model encoder, and the trajectory T k Mapped into its trajectory representation, TSC embedding i For each category representation of each cluster, m represents the number of fused road network representations in the area. As the network is updated and iterated (optimization function Loss), the trajectory representation will be mapped to the corresponding cluster representation TSCembedding i In this way, the representation of the road network trajectory is learned.

[0078] Step S107: Generate new trajectory data through the Transformer model according to the above fusion representation, and integrate and generate the corresponding road network topology map through learning the real grid map corresponding to the real road network map.

[0079]

[0080] The process shown above realizes the regeneration of multimodal fusion trajectory, where RT embedding is the trajectory grid representation in the region. i is the representation of each trajectory category of the fusion. i The road network trajectory generated by the model, It is represented as the fusion of the rasterized real road network trajectory representation and each trajectory category representation, which is matrix addition, so as to obtain the global trajectory clustering representation, and finally generate the road network trajectory points through the transformer decoder.

[0081] Troad_map={(f(xi,yi))|(xi,yi)∈Tk}

[0082]

[0083] The process shown above realizes the learning process of the generated road network trajectory and the real road network, where T k is the actual trajectory point, including longitude and latitude (x i ,y i ), T road_map is a road network trajectory point, which passes through the coordinate point (u i ,u j ) is matched with the real trajectory points to select the matching road network trajectory points. Finally, the generated trajectory points are aligned with the road network trajectory points through the optimization function Loss (n is the number of trajectory points in the area) to determine the accuracy of the road network representation.

[0084] The obtained trajectory cluster representation is decoded to generate the corresponding trajectory data, and the generated trajectory data is obtained by learning with the real grid map generated by the real road network map. Finally, the corresponding road network map is generated by integrating the trajectory data.

[0085] See also Figure 3, is a schematic diagram of the road network topology map effect provided by an embodiment of the present invention, in which respectively are the real road network map corresponding to the trajectory, i.e. the true value, the picture corresponding to the trajectory data, i.e. the trajectory point, and the generated grid road network map, and the road network topology map generated by the present invention. The road network topology map has a high degree of consistency with the real road network map, which reflects the accuracy of the road network topology map generated by the present invention.

[0086] As can be seen from the description of the above embodiments, a method for generating a road network topology map provided by an embodiment of the present invention includes obtaining trajectory data; generating a trajectory grid map according to the trajectory data; obtaining a time-series trajectory representation of each trajectory according to the trajectory data; obtaining a trajectory grid representation of each cluster category according to the trajectory grid map; determining the cluster category of each trajectory by the similarity between the time-series trajectory representation and the trajectory grid representation of each trajectory; fusing the trajectory grid representation corresponding to the determined cluster category with the time-series trajectory representation to obtain a fused representation; training the model according to the fused representation, and finally generating the corresponding road network topology map. The present invention fuses the image data and the time-series trajectory data represented by the trajectory, thereby enhancing the representation capability of the road network topology map and effectively improving the accuracy. In addition, the end-to-end generation method does not require additional algorithms, thereby improving the generation efficiency.

[0087] Through the description of the above method embodiments, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a 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: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0088] An embodiment of the present invention provides a non-volatile computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the road network topology map generating method in any of the above method embodiments.

[0089] Corresponding to the embodiment of the battery health status estimation method provided by the present invention, the present invention also provides a road network topology map generating device.

[0090] See also Figure 4 , is a schematic diagram of the structure of a road network topology map generating device provided by an embodiment of the present invention. As shown in the figure, the device includes:

[0091] An acquisition module 11, used for acquiring trajectory data;

[0092] A trajectory grid map generating module 12, used to generate a trajectory grid map according to the trajectory data;

[0093] A time series trajectory representation module 13, used for inputting the trajectory data into a Transformer model to obtain a time series trajectory representation of each trajectory;

[0094] A trajectory grid representation module 14, used for inputting the trajectory data into the VIT model, and obtaining the trajectory grid representation of each cluster category represented by the trajectory grid diagram through MLP;

[0095] A clustering module 15 is used to calculate the similarity between the temporal trajectory representation and the trajectory grid representation of each trajectory, and determine the clustering category of each trajectory;

[0096] A fusion representation module 16, used to fuse the trajectory grid representation corresponding to the determined cluster category with the time series trajectory representation to obtain a fusion representation of each trajectory;

[0097] The generation module 17 is used to generate new trajectory data through the Transformer model according to the fusion representation, and integrate and generate the corresponding road network topology map through learning the real road network map corresponding to the real grid map.

[0098] In an exemplary embodiment, the trajectory grid map generating module 12 is further used for:

[0099] Splitting the trajectory data into trajectory points of preset length;

[0100] Calculate the similarity of each trajectory point according to the Frechet distance, delete the trajectory point and update the trajectory data;

[0101] Generate a trajectory grid map based on the updated trajectory data.

[0102] In an exemplary embodiment, the clustering categories include a combination of one or more of a straight trajectory category, a left turn trajectory category, and a right turn trajectory category.

[0103] In an exemplary embodiment, the fusion characterization module 16 is further configured to:

[0104] Add the trajectory grid representation and time series trajectory representation of the same cluster category to obtain the fused representation of each trajectory;

[0105] or,

[0106] The attention module is used to fuse the trajectory grid representation and time series trajectory representation of the same clustering category to obtain the fused representation of each trajectory.

[0107] Figure 5 is a schematic diagram of the hardware structure of an electronic device for executing a method for generating a road network topology map provided by an embodiment of the present invention, such as Figure 5 As shown, the device includes:

[0108] One or more processors 510 and memory 420, Figure 5 A processor 510 is taken as an example.

[0109] The device for executing the road network topology map generating method may further include: an input device 530 and an output device 540 .

[0110] The processor 510, the memory 520, the input device 530 and the output device 540 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0111] The memory 520 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the road network topology map generation method in the embodiment of the present invention (for example, the attached Figure 4 The processor 510 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 520, that is, the road network topology map generation method of the above method embodiment is realized.

[0112] The memory 520 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the processing device of the road network map generation method, etc. In addition, the memory 520 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 520 may optionally include a memory remotely arranged relative to the processor 510, and these remote memories may be connected to the processing device of the road network topology map generation method 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.

[0113] The input device 530 can receive input digital or character information, and generate key signal input related to user settings and function control of the processing device for generating the road network topology map. The output device 540 can include display devices such as a display screen.

[0114] The one or more modules are stored in the memory 520, and when executed by the one or more processors 510, the road network topology map generating method in any of the above method embodiments is executed.

[0115] The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0116] The electronic device of the embodiment of the present invention exists in various forms, including but not limited to:

[0117] (1) Mobile communication devices: These devices are characterized by having mobile communication functions and their main purpose is to provide voice and data communications. These terminals include: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones.

[0118] (2) Ultra-mobile personal computer devices: These devices fall into the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access features. These terminals include: PDA, MID and UMPC devices, such as iPad.

[0119] (3) Portable entertainment devices: These devices can display and play multimedia content. They include audio and video players (such as iPods), handheld game consoles, e-books, smart toys, and portable car navigation devices.

[0120] (4) Server: A device that provides computing services. The server consists of a processor, hard disk, memory, system bus, etc. The server is similar to a general computer architecture, but because it needs to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0121] (5) Other electronic devices with data interaction functions.

[0122] The device embodiments described above are merely illustrative, wherein 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, i.e., they may be located in one place or distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0123] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0124] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0125] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for generating a road network topology map, characterized in that: include: Get trajectory data; Generate a trajectory grid map according to the trajectory data; Inputting the trajectory data into the Transformer model to obtain a temporal trajectory representation of each trajectory; Input the trajectory grid graph into the VIT model, and obtain the trajectory grid representation of each cluster category represented by the trajectory grid graph through MLP; Calculate the similarity between the time series trajectory representation and the trajectory grid representation of each trajectory, and determine the clustering category of each trajectory; The trajectory grid representation and the time series trajectory representation corresponding to the determined clustering category are fused to obtain a fused representation of each trajectory; New trajectory data is generated through the Transformer model according to the fusion representation, and the corresponding road network topology map is generated through integration through learning of the real grid map corresponding to the real road network map.

2. The method for generating a road network topology map according to claim 1, characterized in that: Generating a trajectory grid map according to the trajectory data includes: Splitting the trajectory data into trajectory points of preset length; Calculate the similarity of each trajectory point based on the Frechet distance, delete abnormal trajectory points and update the trajectory data; Generate a trajectory grid map based on the updated trajectory data.

3. The method for generating a road network topology map according to claim 1 or 2, characterized in that: The clustering categories include a combination of one or more of a straight trajectory category, a left turn trajectory category, and a right turn trajectory analogy.

4. The method for generating a road network topology map according to claim 1 or 2, characterized in that: The step of fusing the trajectory grid representation corresponding to the determined cluster category with the time series trajectory representation to obtain a fused representation of each trajectory includes: Add the trajectory grid representation and time series trajectory representation of the same cluster category to obtain the fused representation of each trajectory; or, The attention module is used to fuse the trajectory grid representation and time series trajectory representation of the same clustering category to obtain the fused representation of each trajectory.

5. A road network topology map generating device, characterized in that: include: An acquisition module, used to obtain trajectory data; A trajectory grid map generating module, used to generate a trajectory grid map according to the trajectory data; A time series trajectory representation module, used for inputting the trajectory data into a Transformer model to obtain a time series trajectory representation of each trajectory; A trajectory grid representation module, used for inputting the trajectory grid graph into a VIT model, and obtaining a trajectory grid representation of each cluster category represented by the trajectory grid graph through MLP; A clustering module, used to calculate the similarity between the time series trajectory representation and the trajectory grid representation of each trajectory, and determine the clustering category of each trajectory; A fusion representation module, used to fuse the trajectory grid representation corresponding to the determined cluster category with the time series trajectory representation to obtain a fusion representation of each trajectory; A generation module is used to generate new trajectory data through a Transformer model according to the fusion representation, and integrate and generate a corresponding road network topology map after learning the real grid map corresponding to the real road network map.

6. The road network topology map generating device according to claim 5, characterized in that: The trajectory grid map generation module is also used for: Splitting the trajectory data into trajectory points of preset length; Calculate the similarity of each trajectory point according to the Frechet distance, delete the trajectory point and update the trajectory data; Generate a trajectory grid map based on the updated trajectory data.

7. The road network topology map generating device according to claim 5 or 6, characterized in that: The clustering categories include a combination of one or more of a straight trajectory category, a left turn trajectory category, and a right turn trajectory analogy.

8. The road network topology map generating device according to claim 5 or 6, characterized in that: The fusion representation module is also used for: Add the trajectory grid representation and time series trajectory representation of the same cluster category to obtain the fused representation of each trajectory; or, The attention module is used to fuse the trajectory grid representation and time series trajectory representation of the same clustering category to obtain the fused representation of each trajectory.

9. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the road network topology map generating method as claimed in any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the steps of the road network topology map generation method as described in any one of claims 1 to 4.

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