Data processing method, navigation method, and computer program product

Abstract

The embodiments of the present application provide a data processing method, an electronic device, and a computer program product. The method includes: acquiring a road video, an original position of a first image acquisition device in a road network coordinate system, and a road associated with the road video, wherein the first image acquisition device is used to acquire the road video; acquiring a high-precision map image related to the road video according to the original position of the first image acquisition device and the road associated with the road video; determining a calibration position of the road video according to the road video, the high-precision map image, and the acquisition position of the high-precision map image; determining the output road video based at least on the calibration position; and generating output standardized video data according to the output road video. The method can standardize different road videos.

Description

Technical Field

[0001] Embodiments of the present application relate to the technical field of dynamic traffic processing, and in particular, to a data processing method, a navigation method, and a computer program product. Background Art

[0002] In existing traffic scenarios, users mainly learn about road conditions through the traffic function of map application software. However, this traffic function can only provide information on whether a certain road section is congested. Users cannot directly see the actual driving conditions of a certain road section or intersection. When encountering congestion during driving, it is usually difficult to know the cause of the congestion. With the development of video technology, users hope to be able to view the actual driving conditions of a certain road section or road condition in a timely and intuitive manner by watching road videos, so as to make better plans for driving or travel. Due to the different image quality of videos collected by image acquisition devices on different roads, and the inaccuracy of relevant information of the video (such as acquisition location, etc.), existing road videos are difficult to convey to users. Summary of the invention

[0003] In view of this, embodiments of the present application provide a data processing method, an electronic device, and a computer program product to at least partially solve the above problems.

[0004] According to a first aspect of an embodiment of the present application, a data processing method is provided, comprising: acquiring a road video, an original position of a first image acquisition device in a road network coordinate system, and a road associated with the road video, the first image acquisition device being used to acquire the road video; acquiring a high-precision map image related to the road video based on the original position of the first image acquisition device and the road associated with the road video; determining a calibration position of the road video based on the road video, the high-precision map image, and the acquisition position of the high-precision map image; determining the output road video based at least on the calibration position; and generating output standardized video data based on the output road video.

[0005] According to a second aspect of an embodiment of the present application, a data processing device is provided, comprising: a first acquisition module, used to acquire a road video, an original position of a first image acquisition device in a road network coordinate system, and a road associated with the road video, the first image acquisition device being used to acquire the road video; a second acquisition module, used to acquire a high-precision map image related to the road video based on the original position of the first image acquisition device and the road associated with the road video; a first determination module, used to determine a calibration position of the road video based on the road video, the high-precision map image and the acquisition position of the high-precision map image; a second determination module, used to determine the output road video based at least on the calibration position; and an output module, used to generate output standardized video data based on the output road video.

[0006] According to a third aspect of an embodiment of the present application, there is provided an electronic device comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform an operation corresponding to the data processing method of the first aspect.

[0007] According to a fourth aspect of an embodiment of the present application, a computer program product is provided. When the computer program product is executed by a processor, it implements the data processing method of the first aspect.

[0008] The data processing method provided in the embodiment of the present application unifies the position of the first image acquisition device of the road video into the road network coordinate system, thereby realizing the unity of the coordinate system of the original positions of different road videos, thereby improving the degree of standardization, and correcting the original position based on the original position in combination with the roads, high-precision map images and their acquisition positions associated with the road network to obtain a calibration position, and determining the exportable road video according to the calibration position, thus ensuring the safety of the exportable road video. Generate exportable standardized video data based on the road video, and users can view it later, so that users can obtain the road conditions and real driving environment in a timely and rapid manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0010] Figure 1 A schematic diagram of a scenario of a data processing method provided in Example 1 of the present application;

[0011] Figure 2A A flowchart of a data processing method provided in Embodiment 2 of the present application;

[0012] Figure 2B A schematic diagram of a scenario of a data processing method provided in Embodiment 2 of the present application;

[0013] Figure 3 A flowchart of a navigation method provided in Embodiment 3 of the present application;

[0014] Figure 4 A structural diagram of a data processing device provided in Embodiment 4 of the present application;

[0015] Figure 5 A structural diagram of a navigation device provided in Embodiment 5 of the present application;

[0016] Figure 6 This is a structural diagram of an electronic device provided in Example 6 of the present application. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the embodiments of the present application should fall within the scope of protection of the embodiments of the present application.

[0018] The specific implementation of the embodiment of the present application is further explained below in conjunction with the accompanying drawings of the embodiment of the present application.

[0019] Embodiment 1

[0020] The first embodiment of the present application provides a data processing method. For ease of understanding, the application scenario of the method provided in the first embodiment of the present application is described as follows:

[0021] In order to ensure driving safety on some hot roads and highways, a first image acquisition device (such as a camera) is set on the road or around the road, and the first image acquisition device is used to collect video of the road (hereinafter referred to as road video for the sake of clarity). Due to differences in the video request protocols used by different road video providers and the field definitions in the protocols, the playback quality and format of road videos are also different, making it difficult to share existing road videos with users for viewing. In order to solve this problem, in the usage scenario of this application, road videos from different providers are processed in a standardized and unified manner to obtain exportable road videos, so that users can view road videos of roads of interest through live broadcasts and other methods, thereby conveniently and timely understanding road conditions.

[0022] The method provided in Example 1 of the present application is described in detail below in combination with the aforementioned usage scenarios.

[0023] like Figure 1 As shown, it shows a schematic diagram of the step flow of the method, which includes the following steps:

[0024] Step S102: Acquire a road video, an original position of a first image acquisition device in a road network coordinate system, and a road associated with the road video.

[0025] The first image acquisition device is used to acquire the road video. For example, the first image acquisition device may be a camera installed on the road.

[0026] In order to adapt to the problem that different road video providers use different video request protocols and define different fields in the protocol, road videos are requested from providers according to the video request protocols of different providers to obtain relevant information of the road video. The relevant information includes but is not limited to: the reading address of the road video, the positioning position of the first image acquisition device for acquiring the road video in the provider's coordinate system, the encoding format of the road video and other information.

[0027] The road video can be obtained based on the read address of the road video. The first image acquisition device can be converted into the road network coordinate system based on its positioning position in the provider's coordinate system, and the converted position is used as the original position of the road video, so that the first image acquisition device and the road video can be associated with the road in the road network.

[0028] In order to ensure data security, the road network coordinate system can be an encrypted Martian coordinate system. In this way, the unified position expression method can be achieved while ensuring data security. Even if the positioning position of the first image acquisition device of some road videos is expressed in the form of mileage from the reference point, and the positioning position of the first image acquisition device of some road videos is expressed in the form of longitude and latitude, they can be unified into one representation method to facilitate subsequent processing.

[0029] Since the positioning position may have accuracy issues, such as the positioning position in the relevant information of the road video is not accurate enough, in this case, the original position of the first image acquisition device in the road network coordinate system obtained by mapping the positioning position to the road network coordinate system is also inaccurate. In order to improve the accuracy, the road video can be subsequently associated with the roads in the road network according to steps S104 and S106, and the original position can be corrected in combination with the high-precision map image collected by the second image acquisition device (such as a high-precision map collection vehicle).

[0030] Step S104: Acquire a high-precision map image related to the road video according to the original position of the first image acquisition device and the road associated with the road video.

[0031] When collecting high-precision map images, the second image acquisition device (such as a high-precision map vehicle) travels along the road and collects scenes and objects on and around the road, and at the same time positions the second image acquisition device to obtain the collection position of the high-precision map image. The positioning result of the high-precision map acquisition device forms trajectory information, which includes a series of trajectory points and the positioning time corresponding to each trajectory point.

[0032] Based on the original position of the first image acquisition device and the roads associated with the road video, high-precision map images belonging to the associated roads and whose acquisition positions are near the original positions (such as within 100 meters, 500 meters or 1000 meters of the original positions) can be screened out from the captured high-precision map images.

[0033] Step S106: Determine a calibration position of the road video according to the road video, the high-precision map image, and the acquisition position of the high-precision map image.

[0034] One way to determine the calibration position may be to determine the captured position of the second image acquisition device in the road network coordinate system at that time through the time when the second image acquisition device appears in the road video and the trajectory information of the second image acquisition device.

[0035] By identifying the high-precision map image, it is possible to determine the high-precision map image where the first image acquisition device appears. Combined with the acquisition position and the photographed position of the high-precision map image, the real position of the first image acquisition device is determined as the calibration position. In this way, the calibration position of the first image acquisition device can be ensured to be accurate, thereby ensuring the accuracy of the video viewed by subsequent users.

[0036] Step S108: Determine the road video to be output based at least on the calibration position.

[0037] In one feasible manner, a road video corresponding to a road section with a large traffic volume is selected as the output road video according to the calibration position, because such road section has a large number of users watching. Of course, in other embodiments, the output road video may also be determined in other ways, such as selecting a road video with a higher security as the output road video for reasons such as information security, and there is no limitation to this.

[0038] Step S110: generating outputtable standardized video data according to the output road video.

[0039] The road video that needs to be output can be further processed to form standardized video data that can be output so that it can be output more conveniently, thereby meeting the viewing needs of users.

[0040] Through this embodiment, by unifying the position of the first image acquisition device of the road video into the road network coordinate system, the coordinate system of the original position of different road videos is unified, thereby improving the degree of standardization, and the original position is corrected based on the original position combined with the roads, high-precision map images and their acquisition positions associated with the road network to obtain a calibration position, and the output road video is determined according to the calibration position, thus ensuring the safety of the output road video. Outputtable standardized video data is generated based on the road video, and users can view it later, so that users can obtain the road conditions and real driving environment in a timely and rapid manner.

[0041] Embodiment 2

[0042] Reference Figure 2A , showing a step flow chart of the data processing method of embodiment 2 of the present application.

[0043] For ease of understanding, before describing the implementation process of the method in detail, a brief description of its execution subject is given. The method of this embodiment can be executed by a data processing system, which can be deployed on the big data computing middleware of the cloud service system. This can better adapt to the big data operation scenario, realize automatic operation at the daily level, and also help improve load balancing performance, thereby improving overall robustness. Logically, if Figure 2B As shown, the data processing system includes three main parts: access layer, processing layer and control layer. The access layer is used to connect with different road video providers to request road videos and related information from different providers, and to standardize road videos and / or road video related information to facilitate the processing layer to further process the road videos. The processing layer is mainly used to screen and process road videos to meet the needs of subsequent exposure to users. The control layer is mainly used to control whether the processed road videos are output.

[0044] In a specific example, the method includes the following steps:

[0045] Step S202: Acquire a road video, an original position of a first image acquisition device in a road network coordinate system, and a road associated with the road video.

[0046] In this embodiment, the first image acquisition device is used to acquire the road video. For example, the first image acquisition device is a camera installed on a road (such as a highway or a road in a city).

[0047] In a feasible manner, in order to adapt to different providers to ensure that the road video can be reliably requested, the access layer is used to perform step S202, and step S202 is implemented by the following sub-steps (not shown in the figure):

[0048] Sub-step S2021: using a video transmission protocol corresponding to the road video, obtaining the road video and the positioning position of the first image acquisition device of the road video in the installation coordinate system.

[0049] When the road video is provided by multiple different providers, an instruction for requesting the video can be generated for each provider according to the communication mode and field definition specified in the video transmission protocol specified by the provider, and the instruction for requesting the video is sent to the provider to obtain a response message returned by the provider. The response message includes but is not limited to: the read address of the road video, the positioning position of the first image acquisition device, and the installation coordinate system used for the positioning position. The installation coordinate system can be the coordinate system of the provider.

[0050] Road video can be obtained by reading the address.

[0051] Sub-step S2022: Mapping the positioning position into the road network coordinate system to obtain the original position of the first image acquisition device in the road network coordinate system.

[0052] The road network coordinate system can be an encrypted Martian coordinate system to improve security and prevent leakage of the real geographical location. A feasible mapping method can be: determine the conversion relationship between the provider's coordinate system and the original coordinate system corresponding to the Martian coordinate system, map the positioning position to the original coordinate system according to the conversion relationship, obtain the mapped position, and encrypt the mapped position according to the encryption method from the original coordinate system to the Martian coordinate system to obtain the original position.

[0053] Sub-step S2023: Matching the original position road network of the first image acquisition device to obtain a road associated with the road video acquired by the first image acquisition device.

[0054] The road network data includes the location of the road. Based on the location of the road, the road where the original location is located can be determined to determine the road associated with the road video.

[0055] Step S204: Acquire a high-precision map image related to the road video according to the original position of the first image acquisition device and the road associated with the road video.

[0056] In a feasible manner, steps S204 to S208 may be implemented by the processing layer. Step S204 may filter out high-precision map images located within the associated road according to the acquisition location of the high-precision map images, and then select high-precision map images whose distance from the original location is within a certain distance threshold from these high-precision map images as the relevant high-precision map images.

[0057] Of course, in other implementations, other methods may also be used to determine the relevant high-precision map images, and there is no limitation to this.

[0058] Step S206a: Determine a calibration position of the road video according to the road video, the high-precision map image, and the acquisition position of the high-precision map image.

[0059] In one feasible manner, step S206a may be implemented by the following sub-steps:

[0060] Sub-step S2061: Determine the high-precision map image captured by the first image acquisition device from the high-precision map image.

[0061] For example, when the second image acquisition device acquires a high-precision map image, the second image acquisition device has an upward-facing camera, so that some high-precision map images above can be acquired, so that when the second image acquisition device passes under the first image acquisition device, an image containing the first image acquisition device can be acquired. By performing image recognition on the high-precision map image, it can be determined that the high-precision map image captured by the first image acquisition device.

[0062] Sub-step S2062: Determine the calibration position of the road video according to the acquisition position of the high-precision map image captured by the first image acquisition device and the time when the second image acquisition device appears in the road video.

[0063] Since the acquisition position of the high-precision map image is limited by positioning accuracy and other reasons, there may be a certain deviation between the acquisition position and the actual real position. In order to solve this problem, image recognition can also be performed on the road video to determine the time when the second image acquisition device appears in the road video. According to the time and the trajectory information of the second image acquisition device, the trajectory point of the second image acquisition device at that time can be determined, and the position corresponding to the trajectory point is used as the photographed position. The calibration position is determined based on the acquisition position and the photographed position.

[0064] Of course, in other embodiments, the calibration position of the first image acquisition device may also be manually located by manually viewing the road video and the high-precision map image, and there is no limitation to this.

[0065] Optionally, in order to improve the richness of information, the method further includes at least one of step S206b and step S206c.

[0066] Step S206b: Determine the playback quality of the road video.

[0067] The playback quality of a road video is used to indicate whether the road video meets viewing requirements, such as whether there is no mosaic, screen distortion or occlusion that affects viewing, whether the resolution is sufficient, etc. The playback quality can be determined by a trained neural network model (such as a convolutional neural network model), which identifies whether there are image frames in the road video that do not meet the playback requirements, and determines the playback quality of the road video based on the recognition result.

[0068] Alternatively, in another implementation, the playback quality of the road video may be determined by manual scoring.

[0069] Step S206c: Determine the shooting position of the second image acquisition device when it is captured in the road video based on the time when the second image acquisition device is captured in the road video and the trajectory information of the second image acquisition device; determine the standardized shooting direction and standardized field of view of the first image acquisition device based on the road video, the high-precision map image, the shooting position and the calibration position, and output them as associated information of the standardized video data.

[0070] In one feasible manner, the method for determining the time when the second image acquisition device appears in the road video has been described, so it will not be repeated here. Based on the trajectory information of the second image acquisition device, the position corresponding to the trajectory point of the second image acquisition device at that time can be determined as the photographed position, and the orientation of the first image acquisition device (i.e., the standardized shooting direction) can be determined according to the position deviation between the photographed position and the calibration position.

[0071] The standardized field of view of the road video can be determined by comparing the scenes contained in the high-precision map image, its posture when collected, and the scenes contained in the road video.

[0072] Of course, in other feasible ways, the standardized shooting direction and the standardized field of view can also be manually marked, and there is no limitation on this. By determining the standardized field of view and the standardized shooting direction of the first image acquisition device, on the one hand, the erroneous information can be corrected to ensure the accuracy of the subsequent output data, and on the other hand, standardization can be achieved to solve the problem that different providers have different ways of expressing the field of view and shooting direction.

[0073] Step S208: Determine the road video to be output based at least on the calibration position.

[0074] In order to avoid outputting road videos that are not suitable for transmission, the road videos may be screened. For example, step S208 may be implemented by the following sub-steps (not shown in the figure):

[0075] Sub-step S2081: Determine an output score of the road video based at least on the calibration position and the historical traffic flow of the road where the calibration position is located in the road network.

[0076] Since road videos at different calibration locations receive different levels of user attention, in order to effectively utilize limited bandwidth, electricity and computing power resources and avoid resource waste, the output score of the road video is determined based on the historical traffic flow (such as vehicle flow, pedestrian flow, etc.) at the calibration location. Because roads with high historical traffic flow are more likely to attract users' attention, a higher output score can be given to the road video at such a calibration location. The output score can represent the value of the road video to the user, as well as its value in meeting other service needs.

[0077] Of course, in other implementations, the output score of the road video can also be determined based on historical traffic flow and combined with other attributes, such as security, encryption, etc. The output score is obtained by taking the weighted sum of the historical traffic flow, security score and encryption score.

[0078] Sub-step S2082: Determine the road video to be output according to the output score of the road video and the playback quality.

[0079] In one example, these road videos can be sorted from high to low according to the output scores, and the first N road videos are selected as output road videos. N can be a positive integer. This ensures that the playback quality of the output road videos can meet the requirements and avoids outputting low-quality road videos.

[0080] Alternatively, in another example, the geographical area to which the road video belongs can be determined according to the calibration position, and for the road videos in each geographical area, the road videos whose playback quality meets the requirements are screened out, and then the road videos whose output scores are greater than or equal to the output threshold (which can be determined as needed and is not limited to this) are selected from these road videos as the output road videos. In this way, it can be ensured that the output road videos cover different geographical areas as much as possible, thereby meeting the viewing needs of users in different geographical areas, thereby generating adaptability.

[0081] Of course, in other embodiments, other methods may be used to determine the output road video, which is not limited to this.

[0082] Step S210: generating outputtable standardized video data according to the output road video.

[0083] It should be noted that, in order to improve the data richness of the standardized video data, in one example, the method further includes determining a road scene of the road video.

[0084] Road scenes include, but are not limited to: intersections, hot buildings, highways, accident-prone sections, etc. Road scenes can be determined by a neural network model that is trained to recognize road scenes (such as a convolutional neural network model). Alternatively, in another implementation, the road scene can be obtained from relevant information of the road video provided by the provider, or determined by manually viewing the road video, without limitation. After determining the road scene, standardized video data can be output based on the road scene to improve its information richness, and users can also quickly filter out the standardized video data they need through the road scene.

[0085] Based on this, in one feasible manner, step S210 may include the following sub-steps (not shown in the figure):

[0086] Sub-step S2101: Obtain a display screenshot from the output road video.

[0087] In order to facilitate user viewing, display screenshots can be captured from the road video so that users can obtain the main content of the road video by viewing the display screenshots in a live broadcast room, chat room or video playback website for users to watch.

[0088] The display screenshot of the road video may be one or more screenshots, and the display screenshot may be captured manually, or by an automatic screenshot tool, or by a combination of the two methods.

[0089] The display screenshots can be cropped, formatted, and processed so that their size, resolution, and other aspects meet the display requirements and format.

[0090] Sub-step S2102: Generate output standardized video data based at least on the display screenshot, the output road video, the road scene and the playback quality according to output standardization rules.

[0091] The output standardization rules can be determined as needed, such as appropriately defining different fields of the output, and then generating output standardized video data according to the display screenshot, the output road video, the playback quality of the output road video, the road scene, etc. Of course, in order to increase the richness of information and make it more convenient for users to view, the output standardized video data can also include the standardized shooting direction and standardized field of view of the first image acquisition device.

[0092] Optionally, the method may further include step S212.

[0093] Step S212: determining the output state of the first image acquisition device according to the outputtable standardized video data, and publishing the output state indicating that the outputtable standardized video data is allowed to be published.

[0094] In order to facilitate timely knowing whether the road videos corresponding to different first image acquisition devices are allowed to be output, the output status of the first image acquisition device is determined according to the outputtable standardized video data. For example, if the output status of the first image acquisition device corresponding to the outputtable standardized video data indicates that it is allowed to be released, the output status of the first image acquisition device corresponding to the remaining road videos indicates that it is not allowed to be released. In this way, it is convenient to distinguish between the first image acquisition device that can transmit the road video and the unqualified first image acquisition device. In addition, when the road video status of the first image acquisition device is detected to change, it can also be corrected in time, thereby reducing maintenance costs.

[0095] To ensure security, the exportable standardized video data can also be published to the database and disclosed to the outside after passing the inspection.

[0096] Through this method, the position of the first image acquisition device corresponding to the road video can be corrected, and the road video can be scored for quality and value, so as to screen out road videos suitable for transmission, obtain their display screenshots, and collect and process the display screenshots, so as to achieve standardization and unification of road videos, and perform full-link first image acquisition device status control, etc. Based on the unified and standardized outputtable standardized video data, applications such as road observation video live broadcast can be carried out to improve functional richness, ensure video quality, enhance experience, prevent information errors, and have a low overall process cost, and can well adapt to the addition of new road videos or changes in information of existing first image acquisition devices. In addition, position correction based on high-precision map images effectively improves accuracy.

[0097] Embodiment 3

[0098] Reference Figure 3 , showing a schematic flow chart of steps of a navigation method.

[0099] In this embodiment, the method comprises the following steps:

[0100] Step S302: displaying a navigation interface corresponding to the received navigation information.

[0101] For example, when navigating through a map application, the received navigation information includes a navigation starting point, a navigation end point, a navigation path, etc. In order to facilitate viewing by the user, the navigation path is displayed through the navigation interface.

[0102] Step S304: During the navigation process, display screenshots in the standardized video data obtained by the aforementioned method are displayed.

[0103] During navigation, the user may need to check the road conditions of certain sections ahead. To facilitate viewing by the user, screenshots of the standardized video data are displayed in the navigation interface for easy viewing.

[0104] Embodiment 4

[0105] Reference Figure 4 , shows a structural block diagram of a data processing device according to a fourth embodiment of the present application.

[0106] In this embodiment, the data processing device includes:

[0107] A first acquisition module 402, used to acquire a road video, an original position of a first image acquisition device in a road network coordinate system, and a road associated with the road video, wherein the first image acquisition device is used to acquire the road video;

[0108] A second acquisition module 404 is used to acquire a high-precision map image related to the road video according to the original position of the first image acquisition device and the road associated with the road video;

[0109] A first determination module 406 is used to determine a calibration position of the road video according to the road video, the high-precision map image and the acquisition position of the high-precision map image;

[0110] The second determination module 408 is configured to determine the road video to be output based at least on the calibration position.

[0111] The output module 410 is used to generate output standardized video data according to the output road video.

[0112] Optionally, the first acquisition module 402 is used to obtain the road video and the positioning position of the first image acquisition device of the road video in a coordinate system based on a video transmission protocol corresponding to the road video; map the positioning position to the road network coordinate system to obtain the original position of the first image acquisition device in the road network coordinate system; match the original position of the first image acquisition device with the road network to obtain the road associated with the road video acquired by the first image acquisition device.

[0113] Optionally, the first determination module 406 is used to determine a high-precision map image captured by a first image acquisition device from the high-precision map image; determine a calibration position of the road video based on the acquisition position of the high-precision map image captured by the first image acquisition device and the time when the second image acquisition device is captured in the road video, and the second image acquisition device is used to acquire the high-precision map image.

[0114] Optionally, the device further comprises:

[0115] A third determining module 418 determines a shooting position of the second image acquisition device when it is captured in the road video according to the time when the second image acquisition device is captured in the road video and the track information of the second image acquisition device;

[0116] The fourth determination module 412 is used to determine the standardized shooting direction and the standardized field of view of the first image acquisition device according to the road video, the high-precision map image, the photographed position and the calibration position, so as to output the associated information of the standardized data.

[0117] Optionally, the device further comprises:

[0118] A fifth determination module 414, configured to determine at least one of the playback quality and the road scene of the road video;

[0119] The second determination module 408 is used to determine the output score of the road video based at least on the calibration position and the historical traffic flow of the road where the calibration position is located in the road network; and determine the output road video according to the output score of the road video and the playback quality.

[0120] Optionally, the output module 410 is used to obtain a display screenshot from the output road video; and generate output standardized video data according to output standardization rules based at least on the display screenshot, the output road video, the road scene and the playback quality.

[0121] Optionally, the device further comprises:

[0122] The sixth determination module 416 is used to determine the output state of the first image acquisition device according to the exportable standardized video data, and publish the exportable standardized video data that the output state indicates is allowed to be published.

[0123] The data processing device of this embodiment can achieve the corresponding effects of the aforementioned method, so it will not be described in detail.

[0124] Embodiment 5

[0125] Reference Figure 5 , shows a structural block diagram of the navigation device of Example 5 of the present application.

[0126] The first display module 502 is used to display the navigation interface corresponding to the received navigation information.

[0127] The second display module 504 displays display screenshots of the standardized video data obtained in the aforementioned manner during the navigation process.

[0128] Embodiment 6

[0129] Embodiment 6 of the present application provides an electronic device for executing the method described in the above embodiment, referring to Figure 6 , shows a schematic diagram of the structure of an electronic device according to the fourth embodiment of the present application. The specific embodiment of the present application does not limit the specific implementation of the electronic device.

[0130] like Figure 6 As shown, the electronic device 60 may include: a processor (processor) 602 , a communication interface (Communications Interface) 604 , a memory (memory) 606 , and a communication bus 608 .

[0131] in:

[0132] The processor 602 , the communication interface 604 , and the memory 606 communicate with each other via a communication bus 608 .

[0133] The communication interface 604 is used to communicate with other electronic devices or servers.

[0134] The processor 602 is used to execute the program 610, and specifically can execute the relevant steps in any data processing method in the above-mentioned embodiment 1.

[0135] Specifically, the program 610 may include program codes, which include computer operation instructions.

[0136] The processor 602 may be a processor CPU, or an application specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0137] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0138] The program 610 can be specifically used to enable the processor 602 to execute to implement the data processing method described in the first embodiment. The specific implementation of each step in the program 610 can refer to the corresponding description of the corresponding steps and units in the above data processing method embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the above-mentioned method embodiment, which will not be repeated here.

[0139] Embodiment 7

[0140] Based on the method described in the above-mentioned embodiment 1, embodiment 7 of the present application provides a computer storage medium, on which a computer program is stored, and when the program is executed by a processor, the method described in the embodiment is implemented.

[0141] Embodiment 8

[0142] Based on the methods described in the above embodiments, Embodiment 8 of the present application provides a computer program product, which implements the methods described in the embodiments when executed by a processor.

[0143] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0144] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as a computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the data processing method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the data processing method shown here, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the data processing method shown here.

[0145] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present application.

[0146] The above implementation methods are only used to illustrate the embodiments of the present application, and are not limitations on the embodiments of the present application. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the embodiments of the present application. Therefore, all equivalent technical solutions also belong to the scope of the embodiments of the present application. The scope of patent protection of the embodiments of the present application should be limited by the claims.

Claims

1. A data processing method, comprising: Acquire a road video, an original position of a first image acquisition device in a road network coordinate system, and a road associated with the road video, wherein the first image acquisition device is used to acquire the road video; Acquire a high-precision map image related to the road video according to the original position of the first image acquisition device and the road associated with the road video; Determining a calibration position of the road video according to the road video, the high-precision map image, and the acquisition position of the high-precision map image; Determining the road video to be output based at least on the calibration position; Generate exportable standardized video data based on the output road video.

2. The method according to claim 1, wherein: The step of acquiring a road video, an original position of a first image acquisition device in a road network coordinate system, and a road associated with the road video includes: Based on a video transmission protocol corresponding to the road video, obtaining the road video and a positioning position of a first image acquisition device of the road video in an installation coordinate system; Mapping the positioning position into the road network coordinate system to obtain the original position of the first image acquisition device in the road network coordinate system; The original position of the first image acquisition device is matched with the road network to obtain a road associated with the road video acquired by the first image acquisition device.

3. The method according to claim 1, wherein: The determining the calibration position of the road video according to the road video, the high-precision map image and the acquisition position of the high-precision map image includes: Determining a high-precision map image captured by a first image acquisition device from the high-precision map image; The calibration position of the road video is determined based on the acquisition position of the high-precision map image captured by the first image acquisition device and the time when the second image acquisition device is captured in the road video. The second image acquisition device is used to acquire the high-precision map image.

4. The method according to claim 3, wherein: The method further comprises: Determine, according to the time when the second image acquisition device is captured in the road video and the track information of the second image acquisition device, the shooting position of the second image acquisition device when it is captured in the road video; According to the road video, the high-precision map image, the shooting position and the calibration position, the standardized shooting direction and the standardized field of view of the first image acquisition device are determined to be output as associated information of the standardized video data.

5. The method according to claim 4, wherein: The method further comprises: Determining the playback quality of the road video; The determining, based at least on the calibration position, of the road video to be output comprises: Determining an output score for the road video based at least on the calibration location and historical traffic flow of the road in the road network where the calibration location is located; The road video to be output is determined according to the output score of the road video and the playback quality.

6. The method according to claim 5, wherein: The method further comprises: Determining a road scene of the road video; The step of generating outputtable standardized video data according to the output road video comprises: Acquire a display screenshot from the output road video; At least based on the display screenshot, the output road video, the road scene and the playback quality, output standardized video data is generated according to output standardization rules.

7. The method according to claim 6, wherein: The method further comprises: The output state of the first image acquisition device is determined according to the exportable standardized video data, and the output state is published to indicate that the exportable standardized video data can be published.

8. A navigation method, comprising: Displaying a navigation interface corresponding to the received navigation information; During the navigation process, a display screenshot in the standardized video data obtained by the method according to any one of claims 1 to 7 is displayed.

9. A data processing device, comprising: A first acquisition module, used to acquire a road video, an original position of a first image acquisition device in a road network coordinate system, and a road associated with the road video, wherein the first image acquisition device is used to acquire the road video; A second acquisition module, configured to acquire a high-precision map image associated with the road video according to an original position of the first image acquisition device and a road associated with the road video; A first determination module, configured to determine a calibration position of the road video according to the road video, the high-precision map image, and a collection position of the high-precision map image; A second determination module, configured to determine the road video to be output based at least on the calibration position; The output module is used to generate output standardized video data according to the output road video.

10. A computer program product, which, when executed by a processor, implements the data processing method according to any one of claims 1 to 7 or claim 8.

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