A video generation method, apparatus, electronic device, and storage medium

Abstract

The present application discloses a video generation method, apparatus, electronic device, and storage medium, relating to the technical field of vehicle networking. The method includes: in response to a video generation request of a target user for a target trip, presenting a video generation interface to the target user; the video generation interface can be used to present a variety of video attribute options, and each video attribute option can include at least one attribute value of the corresponding video attribute; generating a target video based on the video attribute selection operation of the target user on the video generation interface and the driving data corresponding to the target trip; wherein the driving data can include: data collected by a variety of data collection devices during the target trip. By applying the technical solution of the present application, the video generation requirements of the target user for driving data can be better met.

Description

Technical Field

[0001] This application relates to the technical field of vehicle networking, and in particular, to a video generation method, apparatus, electronic device, and storage medium. Background Art

[0002] With the rise of self-driving tours and short-video culture, people increasingly hope to record beautiful moments (such as driving scenery, etc.) during vehicle driving for journey review or sharing on social platforms.

[0003] Although devices such as dash cams can capture or record these beautiful moments, there are still certain limitations in video recording and personalized expression. For example, the video generation ability is insufficient, and data collection is not comprehensive enough. In this way, the video generation requirements for driving data of users cannot be met. Summary of the Invention

[0004] In view of the above problems, this application provides a video generation method, apparatus, electronic device, and storage medium to better meet the video generation requirements of users for driving data.

[0005] According to one aspect of the embodiments of this application, a video generation method is provided. The method includes:

[0006] In response to a video generation request of a target user for a target itinerary, presenting a video generation interface to the target user; the video generation interface is used to present multiple video attribute options, and each video attribute option includes at least one attribute value corresponding to the video attribute;

[0007] Based on the video attribute selection operation of the target user on the video generation interface and the driving data corresponding to the target itinerary, generating a target video; wherein, the driving data includes: data collected by multiple data collection devices during the target itinerary.

[0008] In an optional manner, before responding to a video generation request of a target user for a target itinerary, it includes:

[0009] Determining multiple types of collected data corresponding to the multiple data collection devices, and respectively setting data collection trigger conditions for the multiple types of collected data;

[0010] Based on multiple data collection trigger conditions and the multiple data collection devices, obtaining the driving data of the vehicle during the target itinerary.

[0011] In an optional manner, based on multiple data collection trigger conditions and the multiple data collection devices, obtaining the driving data of the vehicle during the target itinerary includes:

[0012] If there is at least one start-stop during the vehicle's travel on the target itinerary, based on the multiple data collection trigger conditions and the multiple types of data collection devices, obtain the sub-driving data respectively corresponding to the at least one start-stop.

[0013] Based on the time information respectively corresponding to at least one sub-driving data, perform data aggregation on the at least one sub-driving data to obtain the driving data.

[0014] In an alternative implementation, the method further includes:

[0015] For multiple events included in the driving data, perform the following operations respectively:

[0016] Determine at least one event discrimination method corresponding to an event; each event discrimination method is used to determine whether the event is a special event or a non-special event.

[0017] Based on the at least one event discrimination method, perform event discrimination on the event respectively to obtain at least one event discrimination sub-result.

[0018] Based on the at least one event discrimination sub-result, obtain the event discrimination result of the event.

[0019] In an alternative implementation, based on the at least one event discrimination sub-result, obtaining the event discrimination result of the event includes:

[0020] If there is an event discrimination sub-result indicating that the event is a special event among the at least one event discrimination sub-results, determine that the event discrimination result is that the event is a special event.

[0021] If all of the at least one event discrimination sub-results indicate that the event is a non-special event, obtain the event discrimination result based on the at least one event discrimination sub-result and their respective weight factors; each weight factor represents the discrimination result priority of the corresponding event discrimination method.

[0022] In an alternative implementation, based on the at least one event discrimination sub-result, obtaining the event discrimination result of the event includes:

[0023] Sort the at least one event discrimination sub-results according to the discrimination result priority to obtain a target event discrimination sub-result with the highest discrimination result priority.

[0024] Use the target event discrimination sub-result as the event discrimination result.

[0025] In an alternative manner, based on the video attribute selection operation of the target user on the video generation interface and the driving data corresponding to the target trip, a target video is generated, including:

[0026] Perform data compression on the driving data based on the video duration determined by the video attribute selection operation to obtain the compressed driving data;

[0027] Generate the target video based on the compressed driving data.

[0028] In an alternative manner, generating the target video based on the compressed driving data includes:

[0029] Extend the playing duration corresponding to at least one special event in the compressed driving data to obtain at least one key frame;

[0030] And compress the playing duration corresponding to at least one non-special event in the compressed driving data to obtain at least one non-key frame; the total playing duration of the at least one non-key frame and the at least one key frame is the video duration;

[0031] Generate the target video based on the at least one key frame and the at least one non-key frame.

[0032] In an alternative manner, the method further includes:

[0033] If the video attribute selection operation includes inserting a target event in the driving data, determine the insertion time of the target event in the target video based on the occurrence time of the target event, the start time and end time of the target trip.

[0034] According to another aspect of the embodiments of the present application, a video generation device is provided, and the device includes:

[0035] A display module that, in response to a video generation request of a target user for a target trip, presents a video generation interface to the target user; the video generation interface is used to present a variety of video attribute options, and each video attribute option includes at least one attribute value of the corresponding video attribute;

[0036] A generation module that generates a target video based on the video attribute selection operation of the target user on the video generation interface and the driving data corresponding to the target trip; wherein, the driving data includes: data collected by a variety of data collection devices during the target trip.

[0037] According to another aspect of the embodiments of the present application, an electronic device is further provided, including: a controller; a memory for storing one or more programs, and when the one or more programs are executed by the controller, the above-mentioned video generation method is executed.

[0038] According to still another aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor of a computer, the computer is enabled to execute the above-mentioned video generation method.

[0039] According to yet another aspect of the embodiments of the present application, a computer program product or a computer program is further provided. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above-mentioned video generation method.

[0040] In the video generation method provided by the embodiments of the present application, once a video generation request for a target trip from a target user is received, a video generation interface for customizing video generation can be displayed to the target user. In this way, the target user can select video attributes from at least one attribute value corresponding to various video attribute options presented on the video generation interface, and thus generate a target video that meets the needs of the target user according to the driving data corresponding to the target trip, improving the video generation ability. And since the driving data is data collected by various data collection devices during the target trip, the data collection is relatively complete. Therefore, the video generation requirements of the target user for driving data are better met.

[0041] The above description is only an overview of the technical solutions of the embodiments of the present application. In order to be able to understand the technical means of the embodiments of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the embodiments of the present application more obvious and understandable, the specific embodiments of the present application are hereinafter specifically exemplified. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings are only used to illustrate the embodiments and are not considered as a limitation to the present application. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0043] Figure 1 A flowchart showing a video generation method provided by the present application is shown;

[0044] Figure 2 A schematic diagram of a scenario of aggregating driving data provided by the present application is shown;

[0045] Figure 3Shows a schematic flowchart of a method for determining an event discrimination result of a first event provided by the present application;

[0046] Figure 4 Shows a schematic diagram of a specific application scenario for special event discrimination provided by the present application;

[0047] Figure 5 Shows a method provided by the present application based on Figure 2 Specific scenario schematic diagram;

[0048] Figure 6 Shows a schematic structural diagram of a video generation device provided by the present application;

[0049] Figure 7 Shows a schematic structural diagram of a computer system of an electronic device provided by the present application. Detailed implementation

[0050] Here, an exemplary embodiment will be described in detail, and its examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0051] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0052] The flowcharts shown in the drawings are only exemplary descriptions, not necessarily including all contents and operations / steps, nor necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0053] In the present application, "a plurality of" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0054] The following briefly introduces the design concept of the embodiments of the present application:

[0055] As vehicles are gradually becoming the main or preferred choice for users' travel, it is becoming increasingly important to record the wonderful moments during the journey. However, although devices such as dash cams can be used to capture these moments, users still face many problems in video recording and expression. For example, the problems or defects may include, but are not limited to, the following:

[0056] (1) Insufficient automated video generation ability: Related technologies usually lack the ability to automatically generate videos, resulting in users having to invest a large amount of time and effort in manual editing when organizing and editing the large amount of video materials collected by dash cams.

[0057] (2) Incomplete data collection: Dash cams have limitations in data collection and lack the capture of key features during the journey. For example, abnormal weather changes, significant changes in altitude, and landscape changes along the way. These data are crucial for a complete record of the driving experience.

[0058] (3) Limited user customization: Most dash cam systems provide basic editing functions, lacking advanced editing tools and personalized options, and unable to meet users' needs for in-depth customization of video content.

[0059] (4) Lack of intelligent data aggregation and display: When users are faced with a large amount of video data stored in a memory card (such as a Secure Digital (SD) card) or in the cloud, they lack effective tools to quickly extract and display the highlights of the journey. The lack of intelligent data aggregation and display functions makes it difficult for users to quickly find and display the memorable segments from the massive data.

[0060] In view of this, in order to improve or solve the above problems, the embodiments of this application provide a video generation method to better meet users' video generation requirements for driving data. Please refer to Figure 1 As shown, it is a flowchart of a video generation method provided by the embodiments of this application. In the following introduction process, for the convenience of description and understanding, it is described by taking the application of this video generation method in the server corresponding to the vehicle as an example. As Figure 1 shown, the specific implementation process of this method is as follows:

[0061] S110: In response to a video generation request from a target user for a target journey, display a video generation interface to the target user.

[0062] Among them, the target journey is the journey of the vehicle from the departure place to the destination. For example, the aforementioned target journey can be the journey from the starting place to the tourist destination during a certain tourist trip of the target user.

[0063] The above video generation interface can be used to present various video property options, where each video property option includes at least one property value corresponding to the corresponding video property. Exemplarily, the foregoing various video property options may include, but are not limited to: the map scale corresponding to the custom map created for the target itinerary, the video playback rate, the special event list, the video style, etc.

[0064] The property value corresponding to the map scale can be any value in the range of 1 to 30 km / cm, that is, 1 cm on the custom map corresponds to any value in the range of 1 to 30 km in the target itinerary. If the map scale selected by the target user is 10 km / cm and it is assumed that the target itinerary is 300 km, then it can be determined that the target itinerary on the custom map created for the target itinerary is 30 cm.

[0065] The video playback rate can be related to the map scale. Optionally, the video playback rate is N times the unit map scale / second, where N can be a positive rational number. Still taking the map scale selected by the target user as 10 km / cm as an example, the video playback rate can be 1 to 5 times the map scale unit / second, for example, 2 cm / s. And the video playback rate will affect the video duration of the generated target video. For example, still assuming that the target itinerary is 300 km, then it can be determined that the video duration of the subsequent generated target video is 150 s.

[0066] The video playback rate determined based on the above method will also affect the movement speed of the vehicle on the custom map. If the video playback rate is larger, the vehicle moves faster on the custom map; if the video playback rate is smaller, the vehicle moves slower on the custom map.

[0067] The special event list includes various special events. The target user can select a special event in the special event list, and then add the special event to the video in the subsequent video generation.

[0068] The video style may include, but is not limited to: fresh and energetic style, modern minimalist style, grand and imposing style, emotional resonance style, creative and unique style, etc. The video style can characterize the presentation method of video elements in the generated target video, and can also include background music, etc.

[0069] Thus, through the video generation interface, the target user is allowed to customize the video content. In the video generation interface, each material is displayed with the smallest granularity (i.e., video properties), and the target user can edit and reorganize according to personal needs, thereby realizing the granular display of the materials.

[0070] In addition, the above-mentioned video generation interface is also the user interface (UI), or the user interface can also be called other names such as the first interface. Of course, the embodiments of the present application do not limit this.

[0071] S120: Generate a target video based on the video attribute selection operation of the target user on the video generation interface and the driving data corresponding to the target trip.

[0072] Among them, the driving data may include data collected by various data collection devices on the target trip. Optionally, the foregoing various data collection devices may include, but are not limited to, various sensors or devices related to navigation, trip recording, driving behavior collection, etc.

[0073] For example, during the driving process of the vehicle on the target trip, the vehicle's bus system (such as the controller area network (CAN)) and external sensors can be used to collect vehicle trip and environmental data in real time, and automatically collect the user's driving data and characteristic data on the route according to the navigation, as the relevant materials for automatically generating the video trip finally (that is, the driving data corresponding to the target trip).

[0074] The above-mentioned driving data may include vehicle state data, user behavior data, environmental data, trip event data, etc. Among them, the vehicle state data may include data such as the speed, fuel consumption, and engine state of the vehicle on the target trip. The user behavior data may include data such as the navigation operation and driving mode selection of the target user (such as the driver) on the target trip. The environmental data may include data such as the weather condition, temperature, humidity, altitude, and light during the driving process of the vehicle on the target trip. The trip event data may include data such as special road conditions and traffic events during the driving process of the vehicle on the target trip.

[0075] Exemplarily, the above-mentioned driving data may specifically include 14 types of data: route data, feature data, weather events, altitude changes, landscape features, vehicle state, user behavior, environmental data, tourism activities, digital videorecorder (DVR) data, timestamps, locations, data validity, and user permissions. Optionally, the field names, data types, and field descriptions corresponding to the foregoing 14 types of data are shown in Table 1:

[0076] Table 1 Examples of field names, data types, and field descriptions corresponding to driving data

[0077]

[0078] Among them, structured data represents structured data, enum represents enumerated data, decimal represents decimal data, boolean represents boolean data, video clip represents video clip or video segment data, datetime represents date and time data, and geo - coordinates represents geographical coordinate data.

[0079] In order to obtain relatively rich or complete driving data through the above - mentioned multiple data collection devices. In an alternative implementation, before executing step S120, the server can first determine multiple types of collected data corresponding to the multiple data collection devices, and set data collection trigger conditions for the multiple types of collected data respectively, so as to obtain driving data of the vehicle during the target journey based on the multiple data collection trigger conditions and the multiple data collection devices.

[0080] Each data collection trigger condition can be determined according to the occurrence time of the corresponding data during the target journey. In this way, the server can also determine the opening and closing of the corresponding data collection devices according to the occurrence time of different types of data, so as to avoid the problem that after the vehicle starts driving during the target journey, multiple data collection devices are turned on, which may consume more power to keep the data collection devices in a normal working state to ensure that driving data including multiple data types can be successfully collected.

[0081] For example, for some basic data, the data collection trigger condition for at least one data collection device for collecting basic data is: when the vehicle starts, or when the aforementioned at least one data collection device is initialized. For vehicle status data and environmental data, the data collection trigger condition for at least one data collection device for collecting vehicle status data and environmental data is: during the vehicle's driving process. For special events, the data collection trigger condition for at least one data collection device for collecting special events is: when a special event is encountered. For the end data of the target journey, the data collection trigger condition for at least one data collection device for collecting end data is: when the target journey ends.

[0082] To ensure data security and privacy, the server needs to obtain the explicit authorization of the target user through permission management before it can obtain journey data from the above - mentioned multiple data collection devices. That is, before using the data of the above - mentioned multiple data collection devices, the server needs to prompt the target user to allow relevant permissions, including the acquisition and use of data such as navigation data, driving behavior data, and dashcam data.

[0083] Since the vehicle usually experiences situations such as crossing days or interrupted trips during the journey on the target trip, the target trip is usually recorded as multiple trips. To ensure that when generating the driving data corresponding to the target trip, the complete driving data of the target trip can be accurately and quickly obtained.

[0084] Based on the above-mentioned multiple data collection trigger conditions and the above-mentioned multiple data collection devices, the server can obtain the sub-driving data corresponding to at least one start-stop event that occurred during the vehicle's journey on the target trip, and then, based on the time information corresponding to at least one sub-driving data, perform data aggregation on at least one sub-driving data to obtain the driving data.

[0085] Please refer to Figure 2 As shown, it is a schematic diagram of a scenario for aggregating driving data provided by an embodiment of the present application. Assume that the target trip is from City A to City B, the vehicle's first start time (i.e., the time of departure from City A) is "2024.11.15 13:27:22", the first stop time is "2024.11.15 13:55:37", the second start time is "2024.11.15 13:58:09", the second stop time is "2024.11.15 14:42:12", the third start time is "2024.11.15 14:50:00", and the third stop time (i.e., the time of arrival at City B) is "2024.11.15 16:02:53".

[0086] The server can perform data aggregation on the first sub-driving data corresponding to "2024.11.15 13:27:22 - 2024.11.15 13:55:37", the second sub-driving data corresponding to "2024.11.15 13:58:09 - 2024.11.15 14:42:12", and the third sub-driving data corresponding to "2024.11.15 14:50:00 - 2024.11.15 16:02:53", so as to obtain the driving data of the vehicle from City A to City B.

[0087] Based on the above method, it is possible to aggregate multiple sub-driving data caused by the vehicle starting and then turning off (i.e., powering off) multiple times during the target trip. In other words, since temporary emergencies are likely to occur during the target trip, resulting in the interruption of the target trip, it is supported to aggregate multiple sub-trips into one trip (i.e., the target trip). And, the total time, total mileage, and average speed of multiple sub-trips are used as the time, mileage, and average speed of the final trip (i.e., the target trip). Optionally, if the selected multiple sub-trips cross days, the date element can be reflected in the subsequent generated target video.

[0088] Multi - trip aggregation is applicable to scenarios where refueling, vehicle repair, etc. are carried out at service areas during the journey, resulting in re - setting of the trip, and also applicable to scenarios where the destination is reached over a period of more than one day. Therefore, flexible settings can help target users customize and generate the required video - based trip reports (i.e., driving data).

[0089] Optionally, if the driving duration corresponding to a single sub - trip is short, it can be considered that the sub - trip is not a valid trip, and this sub - trip needs to be aggregated with the adjacent previous sub - trip or the next sub - trip. For example, if the time from power - on to power - off of the vehicle on sub - trip B is within 10 minutes, from the perspective of the trip report, sub - trip B may not be regarded as a valid trip, and sub - trip B needs to be aggregated with the adjacent sub - trip.

[0090] Due to various special events encountered during the vehicle's travel on the target trip, these are usually more likely to be the moments that target users need to record. Therefore, accurately identifying or classifying special events can improve the ability of video generation, and thus better meet the video generation needs of target users.

[0091] In an optional implementation manner, for any one of the multiple events included in the driving data, such as the first event, please refer to Figure 3 As shown, the server performs the following operations:

[0092] S310: Determine at least one event discrimination method corresponding to the first event.

[0093] Exemplarily, when executing step S310, the server can quickly determine at least one event discrimination method corresponding to the first event according to the event type of the first event and the corresponding relationship between the preset event type and the event discrimination method. Of course, other methods can also be used to determine at least one event discrimination method corresponding to the first event, and the embodiments of the present application do not make specific limitations in this regard.

[0094] Among them, each event discrimination method can be used to determine whether the first event is a special event or a non - special event. Exemplarily, the above - mentioned at least one event discrimination method includes, but is not limited to: the event discrimination method based on a binary model, the event discrimination method based on a threshold - based availability evaluation model, and the event discrimination method based on an error - rate - based availability evaluation model, etc.

[0095] The binary model can determine whether the first event is a special event according to a predefined special event set or special event determination rules. Taking the first event as a landscape event as an example, if the first event is a landscape event manually marked by the target user, it can be determined that the first event is a special landscape (i.e., a special event).

[0096] Taking the first event as a weather event as an example, if the first event is a weather event in a preset special weather set, it can be determined that the first event is special weather (i.e., a special event). Among them, the aforementioned preset special weather set can include various special (or extreme) weathers as shown in Table 2.

[0097] Table 2 Examples of various special weathers included in the special weather set

[0098]

[0099] The server can monitor the weather changes during the trip. Any extreme weather shown in Table 2 is regarded as special weather. Moreover, the video data of special weather can be captured by a data collection device (such as a driving recorder) and labeled according to the category of special weather. In addition, the recorded video materials can be automatically horizontally cropped, and the best video clips can be obtained through a set algorithm for storage, so that the special weather can be inserted into the target video as a special event later.

[0100] The availability evaluation model based on a threshold can determine whether the first event is a special event according to the relationship between the first event and the special event discrimination threshold set for the first event. For example, taking the first event as an altitude change, the availability evaluation model based on a threshold can determine whether the first event is a special event according to the set altitude change threshold and the altitude change value corresponding to the first event.

[0101] One or more altitude change thresholds can be set for the target trip, and the altitude threshold can be dynamically set based on the altitude difference of the area where the target trip is located. Using statistical analysis methods, the altitude change distribution of each area (such as a provincial administrative unit) can be determined by calculating the standard deviation from historical driving data. For example, a minimum increment parameter (i.e., the standard deviation) δ (generally between 2% and 4%) is set to determine the altitude change threshold. The specific value of this parameter can be adjusted according to the altitude change characteristics of the area to ensure the scientificity and rationality of the threshold. Then, when the altitude change in the target trip is greater than δ, it can be considered that the altitude change exceeds the threshold and reaches the "special" standard, that is, it can be determined that the first event is a special event; otherwise, it can be determined that the first event is a non-special event.

[0102] For example, as shown in Figure 4 If the maximum altitude difference exceeds 500m during the process of the target trip from place a to place d in region 1, that is, greater than the altitude change threshold δ set for region 1, it can be determined that the altitude change corresponding to the target trip (i.e., the first event) is a special event.

[0103] The availability evaluation model based on error rate mainly targets abnormal services of vehicles. For the abnormal services of vehicles, the server can count the failure rate or error rate within a period of time (e.g., one minute, one hour, or one day). If the error rate of a certain service of the vehicle is higher than the set error rate threshold (e.g., 50%), the event corresponding to this service can be determined as a special event; conversely, if the error rate is lower than the set error rate threshold, the event corresponding to this service can be determined as a non-special event.

[0104] Taking the abnormal traffic service as an example, monitor the road conditions during the journey, obtain real-time data of the journey navigation, such as long congestion, rockfall, car accident, etc., and automatically call the data acquisition device (e.g., driving recorder, etc.) to record and label the traffic conditions, that is, label it as a special event.

[0105] Taking the abnormal vehicle operation as an example, monitor the vehicle operation conditions during the journey, obtain the abnormal conditions and data of the vehicle itself, such as a flat tire of the vehicle (i.e., the tire pressure is 0), vehicle breakdown (e.g., engine failure, braking failure, etc.), automatically call the VHR data and label it, that is, label it as a special event.

[0106] S320: Perform event discrimination on the first event respectively based on at least one event discrimination method to obtain at least one event discrimination sub-result.

[0107] For example, assume that the server determines that the event discrimination methods corresponding to the first event include the event discrimination method based on the binary model, the event discrimination method based on the threshold-based availability evaluation model, and the event discrimination method based on the error rate-based availability evaluation model. Then, the server performs special event discrimination on the first event based on the above 3 event discrimination methods and can obtain 3 event discrimination sub-results.

[0108] S330: Obtain the event discrimination result of the first event based on at least one event discrimination sub-result.

[0109] In an optional implementation manner, when executing step S330, if among the above at least one event discrimination sub-results, there is an event discrimination sub-result indicating that the first event is a special event, the server can determine that the event discrimination result is that the first event is a special event. By adopting this method, it can be determined whether the first event is a special event through any event discrimination method, so as to avoid the situation that a single event discrimination method may fail to timely perform special event discrimination on the first event due to problems such as faults.

[0110] Taking the value of the event discrimination sub-result within [0, 1] as an example, where 0 indicates that the first event is a non-special event and 1 indicates that the first event is a special event. Suppose the special event discrimination threshold set for the event discrimination sub-result is 0.75. If the event discrimination sub-result obtained by the event discrimination method based on the binary model is 0.56, the event discrimination sub-result obtained by the event discrimination method based on the threshold-based availability evaluation model is 0.85, and the event discrimination sub-result obtained by the event discrimination method based on the error rate-based availability evaluation model is 0.67. Then, since the event discrimination sub-result of 0.85 obtained by the event discrimination method based on the threshold-based availability evaluation model is greater than 0.75, it can be determined that the first event is a special event.

[0111] To ensure the accuracy of the event discrimination result of the first event and avoid the problem that the accuracy of a single event discrimination method may be relatively low. The server can combine the three event discrimination sub-results to determine the event discrimination result of the first event. Optionally, if at least one of the above event discrimination sub-results all indicates that the first event is a non-special event, then based on at least one event discrimination sub-result and its corresponding weight factor, the event discrimination result is obtained. The above method for determining the event discrimination result can be specifically expressed as follows:

[0112]

[0113] Among them, represents the event discrimination result of the first event, represents the th event discrimination sub-result corresponding to the event discrimination method, N represents the number of event discrimination methods for performing event discrimination on the first event, represents the weight factor of the event discrimination sub-result , .

[0114] Optionally, the above at least one event discrimination sub-result and its corresponding weight factor can be determined according to the discrimination result priority corresponding to the above at least one event discrimination method. That is, the weight factor of each event discrimination sub-result can represent the discrimination result priority of the corresponding event discrimination method.

[0115] For example, although the binary model, the threshold-based availability evaluation model, and the error rate-based availability evaluation model operate independently, when two or more models are triggered at the same time to perform special event discrimination on the first event, the discrimination result priority can be in turn: binary model > threshold-based availability evaluation model > error rate-based availability evaluation model.

[0116] In an alternative implementation, when performing step S330, the server can also sort the discrimination result priorities of at least one event discrimination sub-result to obtain a target event discrimination sub-result with the highest discrimination result priority, and thus use the target event discrimination sub-result as the event discrimination result. By adopting this method, the event discrimination sub-result obtained by the most reliable event discrimination method can be directly used as the event discrimination result of the first event. Therefore, while improving the efficiency of special event discrimination for the first event, the accuracy of the event discrimination result of the first event is also ensured to a certain extent.

[0117] Based on the above method, after the server completes the event discrimination of whether multiple events included in the driving data are special events, it can display a special event list to the target user during the subsequent generation of the target video. In this way, the target user can quickly screen out the target special event from the special event list, and then add the target special event to the target video.

[0118] Since the time span of the target trip is usually large (e.g., several hours or more than one day), this results in a relatively large amount of driving data corresponding to the target trip. Therefore, in order to more completely display the driving conditions of the vehicle on the target trip within a video with a shorter duration.

[0119] In an alternative implementation, when performing step S120, the server can compress the driving data based on the video duration determined by the video attribute selection operation to obtain the compressed driving data, and thus generate the target video based on the compressed driving data.

[0120] Taking the target trip from City C to City D with a total distance of 1200 km as an example, if the video duration determined by the video attribute selection operation is 20 s, the server can compress the driving data corresponding to the target trip according to a map scale of 30 km / cm and a video playback rate of 2 cm / s, and thus generate the target video based on the compressed driving data. It can be seen that based on the aforementioned data compression method, the sub-driving data of every 60 km can be compressed into 1 s of video data.

[0121] Optionally, there may be a preset mapping relationship between the size of the map scale and the total mileage of the target trip. The aforementioned mapping relationship can be determined according to the actual simulation effect and user feedback. Of course, it can also be determined by other methods, and the embodiments of the present application do not make specific limitations in this regard. For example, the correspondence between the total mileage of the trip and the map scale is shown in Table 3:

[0122] Table 3 Example of the correspondence between the total mileage of the trip and the map scale

[0123]

[0124] Based on the corresponding relationship between the total travel mileage recorded in Table 3 above and the map scale, trips with significantly different total travel mileages can be scaled proportionally according to the corresponding map scale, and ultimately the durations on the video will differ less. Taking a long-distance trip of 300 km as an example, the server restores this long-distance trip on the map at a map scale of 20 km / cm, that is, each centimeter length on the screen corresponds to 20 km of the long-distance trip. Then, the total travel of this long-distance trip is 15 scale units. If the video playback rate progresses at a speed of 2 scale units per second, it can be determined that the total video duration corresponding to this long-distance trip is 7.5 s. Moreover, a 15-cm length change in the video length can clearly represent the entire trip.

[0125] Taking a short-distance trip of 10 km as another example, the server restores this short-distance trip on the map at a map scale of 1 km / cm, that is, each centimeter length on the screen corresponds to 1 km. Then, the total route length of this short-distance trip is 10 scale units. Still assuming that the video playback rate progresses at a speed of 2 scale units per second, it can be determined that the total video duration corresponding to this short-distance trip is 5 s. Therefore, it can be found that the difference in video duration between a 300-km long-distance trip and a 10-km short-distance trip is relatively small.

[0126] As can be seen from Table 3: The ultimately generated video duration is correlated with the total travel mileage, and moreover, the variation range of the travel route (i.e., the map scale) is also positively correlated with the total travel mileage. In addition, the proportional scaling adapts to different trips, and a reasonable screen ratio and video duration can be obtained.

[0127] In order to highlight the key times and scenes corresponding to special events during a special event time period, the playback durations corresponding to at least one special event included in the compressed travel data can be adjusted to reduce the video playback rate during the time period corresponding to the special event.

[0128] Therefore, the server can extend the playback durations corresponding to at least one special event in the compressed driving data to obtain at least one key frame; and compress the playback durations corresponding to at least one non-special event in the compressed driving data to obtain at least one non-key frame; finally, generate a target video based on at least one key frame and at least one non-key frame.

[0129] Optionally, the total playing duration of the at least one non-key frame and the at least one key frame described above is the video duration. That is, the server flexibly adjusts the playing durations corresponding to special events and non-special events respectively. While ensuring that the video duration of the finally generated target video remains unchanged, it extends the playing duration corresponding to special events and shortens the playing duration corresponding to non-special events.

[0130] When identifying and analyzing special events, key frames or time periods with specific meanings or visual attractions can be marked. Without affecting the overall video smoothness, a time aggregation algorithm is adopted to compress the video content of non-key frames to shorten the overall playing time. When the video plays to a key frame or a marked time period, the video playing rate is dynamically reduced to expand the originally compressed time to display more details and information. For special events when the video switches from the compressed state to the slowed-down state, and for non-special events when the video resumes from the slowed-down state to the compressed state, seamless transition technology is used to ensure that the viewing experience of the target users is coherent and natural. The high-quality output of the video is maintained throughout the process to ensure that the clarity and visual effects of key scenes are not affected even after the video playing rate is adjusted.

[0131] Moreover, it can be seen that the video composition elements of the target video can include: a background map and special events. Among them, the background map can be obtained by proportionally scaling the satellite map data corresponding to the target itinerary according to the map scale, restoring the geographical features (such as plains, mountains) in the target itinerary.

[0132] In order to make the content in the subsequent target video more focused and reduce the data volume, the point of interest (POI) data (such as railway stations, parks, scenic spots, and residences, etc.) on the satellite map data can be removed, and only the names of districts / counties, cities, provincial administrative regions, etc. are retained.

[0133] In addition, the video composition elements of the target video may further include: driving route, vehicle model, driving direction, trip data statistics, etc. Among them, the driving route focuses on the starting point to the destination in the target trip, that is, only the user's trip route is concerned, without caring about whether to stop or start on the route. The vehicle model can be constructed from the vehicle model data obtained through the application programming interface (API) provided by the manufacturer. The driving direction is the orientation of the destination relative to the departure place. For example, if the destination city A is to the east of the departure city B, the driving direction of the vehicle in the target video is from west to east, which is consistent with the actual running direction. Otherwise, the driving direction of the vehicle in the target video is from east to west. The trip data statistics may include: total mileage, total driving duration (i.e., total time), average speed, maximum speed, etc. These data reflect the core data of the target trip in the target video. For example, the end of the target video can show the total time, total mileage, and total energy consumption data of this trip.

[0134] In an alternative implementation, if the video attribute selection operation includes inserting a target event in the driving data, the server can determine the insertion time of the target event in the target video based on the occurrence time of the target event, the start time and end time of the target trip, and then insert the target event into the target video at the aforementioned insertion time to ensure the accuracy of video generation. The aforementioned target event can be a special event or a non-special event.

[0135] Exemplarily, the calculation formula for the above insertion time can be expressed as: the insertion time Tx of the target event in the target video = percentage × video duration = (the occurrence time of the target event - the start time of the target trip) / (the end time of the target trip - the start time of the target trip) × video duration. Among them, the start time of the target trip can also be referred to as the start time of the target trip, and the end time of the target trip can also be referred to as the end time of the target trip. The embodiments of the present application do not make any limitations in this regard.

[0136] Please refer to Figure 5 shown, which is a schematic diagram of a specific scenario for video generation provided by an embodiment of the present application. After the server proportionally compresses the driving data corresponding to the target vehicle journey according to the map scale, the compressed driving data with a time span of T0 to Tn can be obtained, where T0 is the time when the vehicle departs from the departure place and Tn is the time when the vehicle reaches the destination. Then, when adding a target event, it can be decided whether to compress or expand the playing duration corresponding to the target event according to whether the target event is a special event. For example Figure 5As shown, the server can insert the aforementioned four target events into the generated video according to the insertion times and event types corresponding to the four target events (i.e., weather 1, weather 2, altitude, and scenery), so as to obtain a target video after inserting the target events. The playback duration span of the target video is T0 to Ty. Optionally, Ty can be equal to T0. In this way, when formulating a video-based driving report (i.e., the target video), it not only ensures that the video content can accurately reflect the driving trajectory of the vehicle (i.e., the target itinerary), but also can display detailed scenes (i.e., special events) at critical moments.

[0137] In summary, in the video generation method provided in the embodiments of the present application, once a video generation request for a target itinerary from a target user is received, a video generation interface for customizing the generated video can be displayed to the target user. In this way, the target user can select video attributes from at least one attribute value corresponding to various video attribute options presented on the video generation interface, so as to generate a target video that meets the needs of the target user according to the driving data corresponding to the target itinerary, improving the video generation ability. And since the driving data includes data collected by various data collection devices during the target itinerary, the data collection is relatively complete, better meeting the target user's video generation requirements for driving data.

[0138] In other words, the embodiments of the present application provide a solution for target users that can automatically collect key itinerary features, provide comprehensive data collection, support highly personalized editing, and have intelligent data processing and display functions, so as to enhance the experience of recording and sharing vehicle travel experiences.

[0139] Among them, the automated data collection and video generation process reduces the time for the target user to edit the video. And the target user can also customize the video content and style according to personal preferences and needs, better meeting the target user's video generation requirements for driving data. Moreover, by collecting key feature data during the itinerary, more comprehensive and rich itinerary data is provided. In addition, intelligent data aggregation technology can also help the target user quickly extract valuable information from a large amount of data. Therefore, a high-quality video-based itinerary report (target video generation) enhances the driving experience and memories of the target user.

[0140] Another aspect of the present application also provides a video generation device. Please refer to Figure 6 As shown, it is a schematic structural diagram of a video generation device provided in the embodiments of the present application. The video generation device 600 includes: a display module 610, a generation module 620, a collection module 630, and a discrimination module 640; where

[0141] A display module 610, in response to a video generation request from a target user for a target trip, presents a video generation interface to the target user; the video generation interface is used to present a variety of video property options, and each video property option includes at least one property value corresponding to the corresponding video property.

[0142] A generation module 620 generates a target video based on the video property selection operation of the target user on the video generation interface and the driving data corresponding to the target trip; wherein, the driving data includes: data collected by a variety of data collection devices during the target trip.

[0143] In an optional manner, before responding to the video generation request from the target user for the target trip, the collection module 630 is specifically configured to:

[0144] Determine a variety of data collection types corresponding to a variety of data collection devices, and respectively set data collection trigger conditions for the variety of data collection types;

[0145] Based on multiple data collection trigger conditions and a variety of data collection devices, obtain the driving data of the vehicle during the target trip.

[0146] In an optional manner, when obtaining the driving data of the vehicle during the target trip based on multiple data collection trigger conditions and a variety of data collection devices, the collection module 630 is specifically configured to:

[0147] If there is at least one start-stop during the driving of the vehicle during the target trip, based on multiple data collection trigger conditions and a variety of data collection devices, obtain sub-driving data corresponding to each start-stop;

[0148] Based on the time information corresponding to at least one sub-driving data, perform data aggregation on at least one sub-driving data to obtain the driving data.

[0149] In an optional manner, the discrimination module 640 is specifically configured to:

[0150] For multiple events included in the driving data, respectively perform the following operations:

[0151] Determine at least one event discrimination method corresponding to an event; wherein each event discrimination method is used to determine whether an event is a special event or a non-special event;

[0152] Based on at least one event discrimination method, respectively perform event discrimination on an event to obtain at least one event discrimination sub-result;

[0153] Based on at least one event discrimination sub-result, obtain the event discrimination result of an event.

[0154] In an alternative manner, when obtaining the event discrimination result of an event based on at least one event discrimination sub-result, the discrimination module 640 is specifically configured to:

[0155] If there is an event discrimination sub-result indicating that an event is a special event among at least one event discrimination sub-results, determine that the event discrimination result is that an event is a special event;

[0156] If all of the at least one event discrimination sub-results indicate that an event is a non-special event, obtain the event discrimination result based on the at least one event discrimination sub-result and their respective corresponding weight factors; where each weight factor represents the discrimination result priority of the corresponding event discrimination method.

[0157] In an alternative manner, when obtaining the event discrimination result of an event based on at least one event discrimination sub-result, the discrimination module 640 is specifically configured to:

[0158] Perform a discrimination result priority ranking on the at least one event discrimination sub-results to obtain a target event discrimination sub-result with the highest discrimination result priority;

[0159] Use the target event discrimination sub-result as the event discrimination result.

[0160] In an alternative manner, when generating a target video based on the video attribute selection operation of the target user on the video generation interface and the driving data corresponding to the target itinerary, the generation module 620 is specifically configured to:

[0161] Perform data compression on the driving data based on the video duration determined by the video attribute selection operation to obtain the compressed driving data;

[0162] Generate a target video based on the compressed driving data.

[0163] In an alternative manner, when generating a target video based on the compressed driving data, the generation module 620 is specifically configured to:

[0164] Extend the playing duration corresponding to at least one special event in the compressed driving data to obtain at least one key frame;

[0165] And compress the playing duration corresponding to at least one non-special event in the compressed driving data to obtain at least one non-key frame; the total playing duration of the at least one non-key frame and the at least one key frame is the video duration;

[0166] Generate a target video based on the at least one key frame and the at least one non-key frame.

[0167] In an alternative manner, the generation module 620 is further configured to:

[0168] If the video attribute selection operation includes inserting a target event in the driving data, the insertion time of the target event in the target video is determined based on the occurrence time of the target event, the start time and the end time of the target trip.

[0169] The video generation device provided in the above embodiment and the video generation method provided in the foregoing embodiment belong to the same concept. The specific manners in which each module and unit perform operations have been described in detail in the method embodiment, and will not be elaborated herein.

[0170] Another aspect of the present application further provides an electronic device. Please refer to Figure 7 shown, which shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application. The specific implementation of the electronic device is not limited in the specific embodiments of the present application.

[0171] Please refer to Figure 7 shown, the electronic device includes: a controller; a memory for storing one or more programs, which when executed by the controller, are used to execute the above-mentioned video generation method. Please continue to refer to Figure 7 shown, the computer system 700 of the electronic device includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 702 or the program loaded from the storage section 708 into the random access memory (RAM) 703, such as executing the method in the above embodiment. In the RAM 703, various programs and data required for system operation are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. The input / output (I / O) interface 705 is also connected to the bus 704.

[0172] Specifically, according to the embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 709, and / or installed from the removable medium 711. When the computer program is executed by the CPU 701, various functions defined in the system of the present application are executed.

[0173] Another aspect of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the video generation method described above is implemented. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist separately without being assembled into the electronic device.

[0174] Another aspect of the present application further provides a computer program product or a computer program. The computer program product or the computer program includes at least one executable instruction. When the executable instruction runs on a video generation device or an electronic device, the video generation device or the electronic device is caused to execute the video generation method described above.

[0175] The computer-readable medium shown in the embodiments of the present application may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two above. The computer-readable storage medium may, for example, be a system, device, or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a RAM, a ROM, an erasable programmable read only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, device, or device. In the present application, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable computer program is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable medium may send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or device. The computer program included on the computer-readable medium may be transmitted by any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0176] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this context, each box in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes may occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, as well as combinations of boxes in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0177] The units involved in the embodiments described in the present application can be implemented in software or in hardware, and the described units can also be provided in a processor. In some cases, the names of these units do not constitute a limitation on the unit itself.

[0178] According to one aspect of the embodiments of the present application, a computer system is also provided, including a CPU that can perform various appropriate actions and processes according to a program stored in a ROM or a program loaded from a storage section into a random access memory (RAM), such as executing the methods in the above embodiments. In the RAM, various programs and data required for system operations are also stored. The CPU, ROM, and RAM are connected to each other via a bus. An I / O interface is also connected to the bus.

[0179] The following components are connected to the I / O interface: an input section including a keyboard, a mouse, etc.; an output section including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section including a hard disk, etc.; and a communication section including a network interface card such as a local area network (LAN) card, a modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as required. A removable medium, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive as required so that a computer program read from it can be installed into the storage section as required.

[0180] The above content is only a preferred exemplary embodiment of the present application and is not used to limit the implementation of the present application. Those of ordinary skill in the art can easily make corresponding adaptations or modifications according to the main concept and spirit of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope required by the claims.

Claims

1. A video generation method, characterized in that, The method includes: In response to a video generation request from a target user for a target itinerary, presenting a video generation interface to the target user; the video generation interface is used to present a variety of video attribute options, and each video attribute option includes at least one attribute value corresponding to the corresponding video attribute. Among them, the variety of video attribute options include: the map scale corresponding to the custom map created for the target itinerary, the video playback rate, the special event list, and the video style. The video playback rate is N times the map scale unit / second, and N is a positive rational number; Based on the map scale and video playback rate corresponding to the target user's video attribute selection operation on the video generation interface, and the total mileage of the target itinerary, determine the video duration; wherein, there is a preset mapping relationship between the map scale and the total mileage of the itinerary; Based on the video duration, perform data compression on the driving data corresponding to the target itinerary to obtain the compressed driving data, and generate a target video based on the compressed driving data; wherein, the driving data includes: vehicle status data, user behavior data, environmental data, and itinerary event data collected by a variety of data collection devices during the target itinerary.

2. The method according to claim 1, characterized in that, Before responding to the video generation request from the target user for the target itinerary, it includes: Determine the variety of collection data types corresponding to the variety of data collection devices, and respectively set data collection trigger conditions for the variety of collection data types; Based on multiple data collection trigger conditions and the variety of data collection devices, obtain the driving data of the vehicle during the target itinerary.

3. The method according to claim 2, wherein Based on multiple data collection trigger conditions and the variety of data collection devices, obtaining the driving data of the vehicle during the target itinerary includes: If there is at least one start-stop during the vehicle's driving on the target itinerary, based on the multiple data collection trigger conditions and the variety of data collection devices, obtain the sub-driving data corresponding to each of the at least one start-stop; Based on the time information corresponding to each of the at least one sub-driving data, perform data aggregation on the at least one sub-driving data to obtain the driving data.

4. The method according to claim 1, wherein The method further includes: For each of the multiple events included in the driving data, perform the following operations respectively: Determine at least one event discrimination method corresponding to one event; wherein each event discrimination method is used to determine whether the one event is a special event or a non-special event; Based on the at least one event discrimination method, respectively perform event discrimination on the one event to obtain at least one event discrimination sub-result; Based on the at least one event discrimination sub-result, obtain the event discrimination result of the one event.

5. The method according to claim 4, wherein Based on the at least one event discrimination sub-result, obtaining the event discrimination result of the one event includes: If among the at least one event discrimination sub-results, there is an event discrimination sub-result indicating that the one event is a special event, then determine the event discrimination result as the one event being a special event; If all of the at least one event discrimination sub-results characterize the one event as a non-special event, then based on the at least one event discrimination sub-result and its corresponding weight factor respectively, obtain the event discrimination result; wherein, each weight factor characterizes the discrimination result priority of the corresponding event discrimination method.

6. The method according to claim 4, wherein Obtaining the event discrimination result of the one event based on the at least one event discrimination sub-result includes: Performing a discrimination result priority sorting on the at least one event discrimination sub-result to obtain a target event discrimination sub-result with the highest discrimination result priority; Taking the target event discrimination sub-result as the event discrimination result.

7. The method according to claim 1, characterized in that, Generating the target video based on the driving data after data compression includes: Expanding the playing duration corresponding to at least one special event in the driving data after data compression to obtain at least one key frame; And, compressing the playing duration corresponding to at least one non-special event in the driving data after data compression to obtain at least one non-key frame; the total playing duration of the at least one non-key frame and the at least one key frame is the video duration; Generating the target video based on the at least one key frame and the at least one non-key frame.

8. The method according to any one of claims 1 to 6, characterized in that, The method further includes: If the video attribute selection operation includes inserting a target event in the driving data, then based on the occurrence time of the target event, the start time and end time of the target trip, determine the insertion time of the target event in the target video.

9. A video generation device, characterized in that, The apparatus includes: A display module, in response to a video generation request of a target user for a target trip, presents a video generation interface to the target user; the video generation interface is used to present a variety of video attribute options, and each video attribute option includes at least one attribute value of the corresponding video attribute, wherein, the variety of video attribute options include: the map scale corresponding to the custom map created for the target trip, the video playback rate, the special event list, and the video style, the video playback rate is N times the map scale unit / second, and N is a positive rational number; A generation module, based on the map scale and the video playback rate corresponding to the video attribute selection operation of the target user on the video generation interface, and the total mileage of the target trip, determine the video duration; wherein, there is a preset mapping relationship between the map scale and the total mileage of the trip; Perform data compression on the driving data corresponding to the target trip based on the video duration to obtain the driving data after data compression, and generate a target video based on the driving data after data compression; wherein, the driving data includes: vehicle state data, user behavior data, environmental data, and trip event data collected by a variety of data collection devices during the target trip.

10. An electronic device, characterized in that, Includes: A controller; A memory, used to store one or more programs, when the one or more programs are executed by the controller, enable the controller to implement the video generation method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, At least one executable instruction is stored in the storage medium, and when the executable instruction runs on the video generation device, it causes the video generation device to perform the operations of the video generation method described in any one of claims 1 to 8.

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