Track generation method and device and electronic equipment

Abstract

The track generation method comprises the following steps: acquiring a track point sequence of a target object in a first time period; on the basis of the track point sequence, a track description text is generated, and the track description text is activity description of the target object in the first time period; and displaying the track description text. Therefore, the track description text capable of describing the activity condition of the object in a period of time is generated from the track point sequence, so that a user can intuitively know the activity condition of the related object in a period of time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence (AI), and in particular to a trajectory generation method and device and electronic equipment. BACKGROUND

[0002] The activity trajectory of an object (such as a person or an object) can record the situation of the object participating in different activities, such as shopping, working or exercising, etc. On the premise of obtaining the authorization of the object and complying with the laws and regulations of the local (i.e. the place where the object is located), the activity trajectory of the object can be obtained through an electronic device (such as a smart phone, a smart watch, etc.) associated with the object. For example, when a user drives a vehicle and uses a navigation software, the software can record the activity trajectory of the vehicle in a specific time period. The activity trajectory of the object is mainly generated through various trajectory points in the activity process, and these trajectory points are usually positioned by means of global positioning system (GPS) positioning or base station positioning technology. However, when viewing the activity trajectory, the related art can only show the approximate path of the object from the starting point to the ending point, and it is difficult to provide the activity situation of the object in this time period. SUMMARY

[0003] The present application provides a trajectory generation method, device, electronic equipment, computer storage medium and computer product, which can provide a trajectory description text of an object in a time period, so as to facilitate a user to understand the activity situation of the object in the time period.

[0004] In a first aspect, the present application provides a trajectory generation method, comprising: obtaining a trajectory point sequence of a target object in a first time period; generating a trajectory description text based on the trajectory point sequence, wherein the trajectory description text is a description of the activity of the target object in the first time period; and displaying the trajectory description text.

[0005] In this way, by generating a trajectory description text from the trajectory point sequence, which can describe the activity situation of the object in a time period, a user can intuitively understand the activity situation of the relevant object in the time period.

[0006] In a possible implementation, the generating of the trajectory description text based on the trajectory point sequence comprises: performing key information extraction on the trajectory point sequence to obtain trajectory key features, wherein the trajectory key features include the position information of the target object in different sub-time periods in the first time period, and the position information of adjacent sub-time periods is different; and obtaining the trajectory description text based on the trajectory key features. In this way, by extracting the trajectory key features from the trajectory point sequence, the information in the trajectory point sequence can be simplified, so as to obtain high-quality data. Further, based on the trajectory key features, a high-quality trajectory description text can be generated.

[0007] In a possible implementation, the method further includes: displaying the track key features.

[0008] In a possible implementation, the track key features further include: an orientation or a direction between the position information of adjacent sub-time periods. In this way, the direction change of the target object in different sub-time periods can be known, and the readability of the track description text generated subsequently can be improved.

[0009] In a possible implementation, before the track point sequence of the target object in the first time period is acquired, the method further includes: detecting a first operation, the first operation being used for viewing the activity of the target object in the first time period.

[0010] In a possible implementation, the processing module is specifically configured to: perform key information extraction on the track point sequence to obtain track key features, wherein the track key features include: position information of the target object in different sub-time periods in the first time period, and the position information of adjacent sub-time periods is different; and obtain the track description text based on the track key features.

[0011] In a possible implementation, the processing module is specifically configured to: perform key information extraction on the track point sequence to obtain track key features, wherein the track key features include: position information of the target object in different sub-time periods in the first time period, and the position information of adjacent sub-time periods is different; and obtain the track description text based on the track key features.

[0012] In a possible implementation, the display module is further configured to: display the track key features.

[0013] In a possible implementation, the track key features further include: an orientation or a direction between the position information of adjacent sub-time periods.

[0014] In a possible implementation, before the track point sequence of the target object in the first time period is acquired, the processing module is further configured to: detect a first operation, the first operation being used for viewing the activity of the target object in the first time period.

[0015] In a possible implementation, the processing module is specifically configured to: perform key information extraction on the track point sequence to obtain track key features, wherein the track key features include: position information of the target object in different sub-time periods in the first time period, and the position information of adjacent sub-time periods is different; and obtain the track description text based on the track key features.

[0016] In a fourth aspect, the present application provides a trajectory generation apparatus, comprising a memory and a processor, the processor executes a computer program stored in the memory to implement the method described in the first aspect or any possible implementation manner of the first aspect. For example, the trajectory generation apparatus can be a chip.

[0017] In a fifth aspect, the present application provides a computer readable storage medium, comprising computer program instructions, when the computer program instructions are executed by a computing device, the computing device executes the method described in the first aspect or any possible implementation manner of the first aspect.

[0018] In a sixth aspect, the present application provides a computer program product comprising instructions which, when executed by a computing device, cause the computing device to perform the method described in the first aspect or any possible implementation manner of the first aspect.

[0019] It can be understood that the beneficial effects of the above-mentioned second aspect to sixth aspect can be referred to the related description in the first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a technical concept schematic diagram of trajectory generation provided by an embodiment of the present application;

[0021] Figure 2 is a flow schematic diagram of a trajectory generation method provided by an embodiment of the present application;

[0022] Figure 3 is a process schematic diagram of key information extraction on a trajectory point sequence provided by an embodiment of the present application;

[0023] Figure 4 is a step schematic diagram of a trajectory generation method provided by an embodiment of the present application;

[0024] Figure 5 is a process schematic diagram of generating a ship sailing trajectory description by a neural network model provided by an embodiment of the present application;

[0025] Figure 6 is a structure schematic diagram of a trajectory generation apparatus provided by an embodiment of the present application;

[0026] Figure 7 is a structure schematic diagram of an electronic device provided by an embodiment of the present application;

[0027] Figure 8 is a structure schematic diagram of another trajectory generation apparatus provided by an embodiment of the present application. DETAILED DESCRIPTION

[0028] The term "and / or" used in the present document is used to describe an associated relationship between objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. The symbol " / " in the present document means an or relationship between associated objects, for example, A / B means A or B.

[0029] The terms "first" and "second" and the like in the description and claims of the present document are used to distinguish different objects, rather than to describe a specific order of the objects. For example, the first response message and the second response message are used to distinguish different response messages, rather than to describe a specific order of the response messages.

[0030] In the embodiments of the present application, the words "exemplary" or "for example" are used to mean serving as an example or illustration. Any embodiment or design presented as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of "exemplary" or "for example" is intended to present concepts in a concrete manner.

[0031] In the description of the embodiments of the present application, unless otherwise specified, "a plurality of" means two or more, for example, a plurality of processing units means two or more processing units, and the like; a plurality of elements means two or more elements, and the like.

[0032] Generally, when a user views the activity track of an object on an electronic device, the electronic device can generate a corresponding activity track for the track points of the object viewed by the user in a corresponding time period. For example, after a user finishes exercising, the user can view the exercise track of himself in the time period through the electronic device; or a guardian can view the activity track of a child or other person being monitored by the guardian in a day on the electronic device, and the like. When the user views the activity track, the electronic device can show the user the activity track of the object viewed by the user in a certain time period, so that the user can intuitively understand the general activity of the object in the time period. In this track display manner, when the user has some understanding of the activity area of the object viewed by the user, the user can understand the activity of the corresponding object through the activity track of the object viewed by the user. However, the user needs to observe the position of the object at a certain time point and the corresponding stay situation, and the like by himself. When the user does not have some understanding of the activity area of the object viewed by the user, the user is difficult to understand the corresponding activity through the activity track.

[0033] Therefore, the present application provides a trajectory generation method, which can generate a trajectory description text of a target object in a time period based on a trajectory point sequence of the target object in the time period. The trajectory description text can be used to describe the activity of the object in the corresponding time period, i.e., the trajectory description text is the activity description of the target object in the time period. In this way, the user can quickly understand the activity of the object in the corresponding time period through the trajectory description text.

[0034] Exemplarily, Figure 1 A technical concept diagram of a trajectory generation method provided by an embodiment of the present application is shown. As shown in the diagram, Figure 1 The overall architecture of the trajectory generation mainly includes a trajectory description generation part. In the trajectory description generation part, the trajectory description text of the target object in a specific time period can be generated by processing the trajectory point sequence of the target object in the time period selected by the user. For example, when the target object is a vehicle and the time period selected by the user is from 15:42 on x month 17 to 16:15, the generated trajectory description text can be as shown in the following table. Figure 1 The trajectory description text shown in the table.

[0035] The specific implementation process of the above technical concept is described below.

[0036] Exemplarily, Figure 2 A flow diagram of a trajectory generation method provided by an embodiment of the present application is shown. It can be understood that the method can be executed by any device, equipment, platform, device cluster with computing and processing capabilities. Exemplarily, the method can be executed by a trajectory generation device, wherein the device can be realized by software and / or hardware, and can be configured in an electronic device, typically, a mobile phone. For ease of description, the following is described by taking an electronic device as an example. As shown in the diagram, Figure 2 The trajectory generation method can include the following steps:

[0037] S201, obtaining a trajectory point sequence of a target object in a first time period.

[0038] In this embodiment, the target object is an object of interest to the user, such as the user's vehicle, the user themselves, or an object under the guardianship of the user as a guardian, etc. The first time period can be a time period selected by the user, such as one hour, one day, one week, or one month. The trajectory point sequence can include at least one trajectory point. The trajectory point sequence can be uploaded by the user; in this case, the electronic device can obtain the trajectory point sequence after the user completes the data upload. Of course, the trajectory point sequence can also be stored in a storage component (such as a local storage module or a cloud server) associated with the electronic device; in this case, after the user selects a time period, the electronic device can read the trajectory point data within that time period from the corresponding storage component. In addition, the electronic device can also obtain the trajectory point sequence from other devices, depending on the actual situation, and is not limited here. In some embodiments, before this step, the user can trigger an operation on the electronic device to view the activity of the target object within the first time period; the electronic device can execute this step after detecting this operation. Of course, the user can also pre-configure to trigger this step after the end of a certain activity, etc., depending on the actual situation, and is not limited here. It should be understood that the trajectory point sequence is obtained with the user's authorization and in compliance with local laws and regulations.

[0039] S202. Based on the trajectory point sequence, generate trajectory description text, where the trajectory description text is a description of the target object's activities within the first time period.

[0040] In this embodiment, the electronic device can process the trajectory point sequence using a neural network model or preset rules to generate trajectory description text. For example, when the target object is a vehicle, the trajectory description text can be as follows: Figure 1 The trajectory description text shown. Figure 1 The trajectory description text shown provides a clear understanding of the vehicle's navigation on the 17th.

[0041] S203. Display the trajectory description text to the user.

[0042] In this embodiment, after processing the trajectory description text, the electronic device can display the description text to the user.

[0043] In this way, by generating trajectory description text from the sequence of trajectory points that describes the activity of an object over a period of time, users can intuitively understand the activity of the relevant object over a period of time.

[0044] In some embodiments, in S202 described above, it is considered that if the track point data is directly input into the neural network model, one or more of the following problems may occur: 1) the neural network model lacks professional spatial information base knowledge and cannot generate accurate positions based on latitude and longitude, which may cause a knowledge illusion problem; 2) the number of track points and the amount of information are huge, far exceeding the processing capacity of the neural network model. For example, in a typical automatic identification system (AIS) track record, the track points of a ship in one day usually range from several thousand to tens of thousands (if it is for multiple days or even a month, the amount of data is even larger), the input sequence given to the neural network model is too long, exceeding the processing capacity of the model; and a large amount of redundant or unimportant track point information also increases the difficulty for the neural network model to understand and reduces the quality of the model generated; 3) the neural network model is difficult to generate spatial directional information of track changes; 4) different scenarios have different description logics, reasoning logics or style requirements; 5) the generated track description text does not meet the requirements. Before being processed by the neural network model, the track point sequence can be subjected to key information extraction to obtain track key features, so that accurate position information can be extracted from the track point sequence, and a large amount of redundant or unimportant track points can be removed to provide high-quality data for subsequent models. Among them, the track key features include: position information of the target object in different sub-time periods in the first time period, and the position information of adjacent sub-time periods is different. In addition, the track key features can also include the orientation or direction between the position information of adjacent sub-time periods. For example, when the first time period is from 3 pm to 5 pm and the target object is a child B monitored by user A, if child B stays in supermarket c from 3 pm to 3:50 pm and stays at home from 4 pm to 5 pm, the track key features extracted from the track point sequence of child B from 3 pm to 5 pm can be: "time: 3 pm to 3:50 pm, location: supermarket c; time: 4 pm to 5 pm, location: home". Since child B stays at the same location from 3 pm to 3:50 pm, the position information in the track point sequence in this time period can be largely repeated, and by performing key information extraction, accurate and simplified data can be obtained to provide high-quality data for subsequent model processing.

[0045] As a possible implementation manner, as Figure 3As shown, the step of extracting key information from the sequence of trajectory points can include: in S301, removing duplicate trajectory points in the sequence of trajectory points to delete the repeated trajectory points. In S302, obtaining the position information corresponding to each trajectory point. The granularity or accuracy of the position information can be determined according to different scene requirements. For example: in the scene of viewing the vehicle trip, the granularity of the position information can be street granularity, township granularity, etc.; in the motion scene, the granularity of the position information can be store granularity, 5-meter interval granularity, etc.; in the ship navigation scene, the accuracy of the position information can be "near A port (or island)", or "3 nautical miles north of A port / island", etc. In addition, when the trajectory point is described by latitude and longitude, the latitude and longitude can also be converted into position information to obtain the position description corresponding to the latitude and longitude. In S303, the trajectory points are sorted in chronological order to facilitate subsequent sampling and processing. In S304, the trajectory points are sampled according to a time sampling strategy such as uniform sampling at a certain time interval, different sampling strategies in different time periods, or different sampling strategies in different location areas. In S305, the trajectory points obtained by time sampling are subjected to position sampling according to a position sampling strategy such as retaining a certain position, ignoring a certain position, merging / simplifying / converting the description of certain positions, retaining or ignoring positions that meet certain conditions (such as staying time exceeding / insufficient xx minutes, etc.). In S306, key information is extracted from the trajectory points obtained by position sampling. Among them, for the trajectories of adjacent time points, if the trajectory positions are the same or meet certain merging rules, the position and time are merged for description to simplify the information; or when an object appears in a certain time period and has the same or similar motion mode or characteristics as before a certain time period, only the consistency, similarity and / or difference points are identified, without completely repeating the description, etc. In S307, the relative orientation between two trajectory points is calculated, such as east, west, south, north, southeast, southwest, northeast, northwest, etc., or the angle value is refined on the basis of the orientation, such as east-north 20 degrees, etc. That is, the relative orientation or position between the trajectory points is calculated. In S308, the number of tokens is calculated. Generally, neural network models have a limit on the number of input tokens, so the number of tokens of the extracted feature information needs to be calculated. In S309, if the number of tokens is within the threshold range, the current feature information meets the requirements, and S311 can be executed; otherwise, S310 is executed. In S310, the sampling strategy is updated (such as increasing the sampling time interval, expanding the granularity of position processing, etc.) and the sampling is re-performed. In S311, the key features of the trajectory are output. It should be understood that, Figure 3 Each step shown in the above can be selected according to actual needs, as long as the key features of the trajectory can be obtained.

[0046] After the trajectory key features are extracted, the trajectory key features can be input into a neural network model for processing to obtain the trajectory description text of the target object in the first time period. Of course, the trajectory key features can also be processed based on preset rules to obtain the trajectory description text of the target object in the first time period.

[0047] In addition, when the trajectory key features need to be extracted, the trajectory key features can also be displayed to the user in S203 described above, so that the user can understand the data referenced for generating the trajectory description text.

[0048] It should be understood that, since the longitude and latitude information of the original trajectory points is large in amount and poor in readability, it is difficult for people to intuitively understand; and the information of the trajectory key features and the trajectory description text is more concise and simple, and has strong readability and understandability. As an important bridge and key intermediate result between the original trajectory points and the trajectory description text, the trajectory key features can also greatly enhance the explainability, traceability and credibility of the generated text.

[0049] In some embodiments, when a neural network model is used to generate the trajectory description text, in order to facilitate the inference of the neural network model, a prompt (Prompt) can be generated based on the extracted trajectory key features, and then the Prompt is input into the neural network model for processing. In different scenarios, there can be different requirements and preferences for the description style of the trajectory description text. For example, taking the task of describing the sailing trajectory of a ship as an example (assuming that the trajectory point information of the ship in a certain time period is given). 1) There is a preference for the degree of detail of the description: in some scenarios, we may want to describe very specifically, such as sailing to x place at x time and x minute, staying in x place for x minutes at x time and x minute, etc.; while in other scenarios, we may just want to describe more generally and have a summary, such as passing through A, B, C, etc. places from x time to x time. 2) There is a preference for reasoning logic: when the ship's activity coordinates change around an island in a certain period of time, some scenarios may want the model to give an inferential description, such as “active around x island from x time to x time”; rather than a more specific factual statement, such as “x time and x minute in x island east x nautical mile, then sail west and south, x time and x minute sail to x island south x nautical mile, x time and x minute sail to west and north, x time and x minute sail to x to west x nautical mile” and the like. Therefore, when generating the Prompt, the Prompt can be constructed based on the trajectory key features and the corresponding style of sample, for example, inputting the trajectory key features and the corresponding style of trajectory description text sample into the Prompt template to obtain the Prompt, so as to generate the trajectory description text consistent with the user's demand, that is, to generate the stylized trajectory description text.

[0050] In addition, to ensure the quality of the trajectory description text, the quality of the trajectory description text generated by the neural network model can also be evaluated. For example, the generated text is evaluated in quality by heuristic rules, neural network models, or expert inspection. As described above, different scenarios may have different style requirements (description logic, summary logic, reasoning logic, writing style, etc.) for trajectory description, which can be sorted out in the form of heuristic rules and form a rule base. Then, based on the rule base, the trajectory description text can be checked for style in a script to evaluate whether it meets the requirements and points that do not meet the requirements / improvements; or the rule set and the trajectory description form a Prompt input neural network model to evaluate whether it meets the requirements and points that do not meet the requirements / improvements.

[0051] When the quality of the trajectory description text meets the requirements, the trajectory description text can be output. When the quality of the trajectory description text does not meet the requirements, the quality evaluation result can be fed back to the neural network model so that the neural network model optimizes the trajectory description text. For example, after obtaining the quality evaluation result of the trajectory description text, if the quality of the trajectory description text does not meet the requirements, the quality evaluation result can be spliced into the Prompt containing the trajectory key features and the corresponding style of the trajectory description text sample to construct an optimized Prompt and input into the neural network model for processing. Of course, the quality evaluation result and the Prompt containing the trajectory key features and the corresponding style of the trajectory description text sample can also be input into the neural network model for processing to generate an optimized Prompt. At this time, the neural network model generates an optimized Prompt that adds specific instructions to the original Prompt, such as: where to improve, etc.

[0052] Further, when the process of generating the trajectory description text includes the steps of Prompt generation, quality evaluation, and feedback optimization, the steps of the trajectory generation method can be changed to the steps shown in Figure 4 In Figure 4 , in S401, the trajectory point sequence is obtained. In S402, the trajectory key features are extracted. In S403, the stylized Prompt is constructed. In S404, the stylized trajectory description text is generated. In S405, the quality of the trajectory description text is evaluated. In S406, when the evaluated quality does not meet the requirements, feedback optimization is performed and the execution of S404 is returned. When the evaluated quality meets the requirements, the trajectory description text is displayed.

[0053] To facilitate understanding of the trajectory generation method provided in the present application, the following will take the generation of a ship navigation trajectory description by a neural network model as an example for introduction.

[0054] For example, asFigure 5 As shown, when the user provides a sequence of trajectory points... Figure 5 As shown in (A), key trajectory features can be extracted to obtain... Figure 5 The trajectory key features are shown in (B). When extracting these key features, techniques such as position transformation, deduplication, time-based sampling, position-based sampling, key information extraction, and orientation calculation can be performed on the trajectory points to extract key trajectory feature information. After extracting the key trajectory features, a stylized Prompt can be constructed. This stylized Prompt can be constructed based on the processed trajectory key features and a Fewshot sample with a specified style (summary logic, reasoning logic, writing style, etc.). Figure 5 After processing the key features of the trajectory shown in (B), we can obtain Figure 5 The stylized Prompt shown in (C) can then be input into a neural network model to generate... Figure 5 The stylized trajectory description text is shown in (D). Figure 5 If the quality of the trajectory description text shown in (D) meets the requirements, such as Figure 5 As shown in (E), the user can be shown the trajectory description text and the extracted key trajectory features (optional).

[0055] It should be understood that, Figure 5 Taking ship trajectory description generation as an example, the difference lies in the stylized reference examples and / or instructions provided when processing aircraft flight trajectories, vehicle driving trajectories, and human activity trajectories. The following mainly provides examples of trajectory description text.

[0056] Aircraft flight path description: Drone X took off from street A at 8:30 on October 11, flew south for 5 minutes, turned southwest at 8:35, crossed street B, and arrived at street C at 8:40 and hovered.

[0057] Vehicle travel trajectory description: Vehicle xx departed from City A at 15:42 on the 17th, traveled north to Block B, turned west at 19:58, crossed Avenue C, and arrived at Street D at 20:25.

[0058] Description of the individual's activity trajectory: Mi Ke departed from home at 8:00 AM on October 10th, walked south, reached xx Park at 10:00 AM, then walked west through the forest, arriving at the foot of the mountain at 3:00 PM. He then climbed the mountain northwards, reaching the summit at 6:00 PM and staying there for an hour. Afterwards, he descended eastwards, returning to the forest at 8:00 PM. Finally, he returned home at 1:00 AM on November 1st.

[0059] It is understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. In addition, the various embodiments or technical features involved in the embodiments described above can be combined according to the actual situation, and the combined solution is still within the protection scope of this application.

[0060] Based on the methods in the above embodiments, this application also provides a trajectory generation device.

[0061] For example, Figure 6 A schematic diagram of the structure of a trajectory generation device provided in an embodiment of this application is shown. Figure 6 As shown, the trajectory generation device 600 includes a processing module 601 and a display module 602. The processing module 601 is used to acquire a sequence of trajectory points of a target object within a first time period. The processing module 601 is also used to generate trajectory description text based on the trajectory point sequence, wherein the trajectory description text is a description of the target object's activities within the first time period. The display module 602 is also used to display the trajectory description text.

[0062] In some embodiments, when generating trajectory description text based on trajectory point sequence, the processing module 601 is specifically used to: extract key information from the trajectory point sequence to obtain trajectory key features, wherein the trajectory key features include: the location information of the target object in different sub-time periods in the first time period, and the location information of adjacent sub-time periods is different; and obtain trajectory description text based on the trajectory key features.

[0063] In some embodiments, the display module 602 is further configured to: display key features of the trajectory.

[0064] In some embodiments, the trajectory key features also include: orientation or direction between location information of adjacent sub-time periods.

[0065] In some embodiments, before acquiring the trajectory point sequence of the target object within a first time period, the processing module 601 is further configured to: detect a first operation, the first operation being used to view the activity of the target object within the first time period.

[0066] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0067] It is understood that the electronic devices involved in the embodiments of this application may be mobile phones, tablets, desktop computers, laptops, handheld computers, notebook computers, ultra-mobile personal computers (UMPCs), netbooks, as well as cellular phones, personal digital assistants (PDAs), augmented reality (AR) devices, virtual reality (VR) devices, artificial intelligence (AI) devices, wearable devices, in-vehicle devices, smart home devices, and / or smart city devices, etc. Exemplary embodiments of electronic devices include, but are not limited to, electronic devices running iOS, Android, Windows, Harmony OS, or other operating systems. The specific type of electronic device is not specifically limited in the embodiments of this application.

[0068] The following describes the electronic device involved in the embodiments of this application. Please refer to... Figure 7 , Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 7 As shown, the electronic device 700 may include a processor 710, a memory 720, and a display screen 730.

[0069] The processor 710 can be a general-purpose processor or a special-purpose processor. For example, the processor 710 may include a central processing unit (CPU) and / or a baseband processor. The baseband processor can be used to process communication data, and the CPU can be used to implement corresponding control and processing functions, execute software programs, and process data from the software programs.

[0070] For example, processor 710 may include one or more processing units. Processor 710 may include one or more of the following: application processor (AP), modem, graphics processing unit (GPU), image signal processor (ISP), controller, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU). Different processors may be independent devices or integrated into one or more processors.

[0071] The memory 720 may store a program, which can be executed by the processor 710 to perform the methods described in the embodiments of this application. The memory 720 may also store data. The processor 710 may read the data stored in the memory 720. The memory 720 and the processor 710 may be configured separately. Optionally, the memory 720 may also be integrated into the processor 710.

[0072] Display screen 730 is used to display images, videos, etc. Display screen 730 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a miniature LED, a microLED, a quantum dot light-emitting diode (QLED), etc. Exemplarily, display screen 730 can be, but is not limited to, a foldable screen, i.e., a display screen capable of being folded. Exemplarily, display screen 730 can be used to display trajectory description text generated by processing module 710, etc.

[0073] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device 700. In other embodiments of this application, the electronic device 700 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0074] Based on the methods described in the above embodiments, this application also provides another trajectory generation apparatus. This trajectory generation apparatus may be, but is not limited to, a chip. Please refer to... Figure 8 , Figure 8 This is a schematic diagram of a trajectory generation device provided in an embodiment of this application. Figure 8 As shown, the trajectory generation device 800 includes one or more processors 801 and interface circuitry 802. Optionally, the trajectory generation device 800 may also include a bus 803. Wherein:

[0075] The processor 801 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed through integrated logic circuits in the hardware of the processor 801 or through software instructions. The processor 801 may be a general-purpose processor, a neural network processing unit (NPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods and steps disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor.

[0076] The interface circuit 802 can be used to send or receive data, instructions or information. The processor 801 can use the data, instructions or other information received by the interface circuit 802 to process the data, instructions or other information, and can send the processed information out through the interface circuit 802.

[0077] Optionally, the trajectory generation device 800 also includes a memory, which may include read-only memory and random access memory, and provides operation instructions and data to the processor. A portion of the memory may also include non-volatile random access memory (NVRAM). This memory may be coupled to the processor 801.

[0078] Optionally, the memory stores executable software modules or data structures, and the processor 801 can execute corresponding operations by calling the operation instructions stored in the memory (which can be stored in the operating system).

[0079] Optionally, the interface circuit 802 can be used to output the execution results of the processor 801.

[0080] It should be noted that the functions of the processor 801 and the interface circuit 802 can be implemented through hardware design, software design, or a combination of hardware and software; no restrictions are imposed here.

[0081] It should be understood that each step of the above method embodiments can be completed by hardware logic circuits or software instructions in a processor.

[0082] Based on the methods in the above embodiments, this application provides a computer-readable storage medium including computer program instructions. When the computer program instructions are executed by a computing device, the computing device performs the methods in the above embodiments; or, when the computer program instructions are executed by a cluster of computing devices, the cluster of computing devices performs the methods in the above embodiments. Exemplarily, the computer-readable storage medium can be any available medium that the computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0083] Based on the methods in the above embodiments, this application provides a computer program product containing instructions that, when executed by a computing device, cause the computing device to perform the methods in the above embodiments, or, when executed by a cluster of computing devices, cause the cluster of computing devices to perform the methods in the above embodiments.

[0084] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0085] The method steps in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0086] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, cloud computing platform, or data center to another website, computer, cloud computing platform, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a cloud computing platform or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0087] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of this application.

Claims

1. A trajectory generation method, characterized in that, The method includes: Obtain the trajectory point sequence of the target object within the first time period; Based on the trajectory point sequence, a trajectory description text is generated, wherein the trajectory description text is a description of the activity of the target object within the first time period; Display the trajectory description text.

2. The method according to claim 1, characterized in that, The step of generating trajectory description text based on the trajectory point sequence includes: Key information is extracted from the trajectory point sequence to obtain trajectory key features, wherein the trajectory key features include: the location information of the target object in different sub-time periods in the first time period, and the location information of adjacent sub-time periods is different; Based on the key features of the trajectory, the trajectory description text is obtained.

3. The method according to claim 2, characterized in that, The method further includes: The key features of the trajectory are shown.

4. The method according to any one of claims 1-3, characterized in that, The key features of the trajectory also include the orientation or direction between the location information of adjacent sub-time periods.

5. The method according to any one of claims 1-4, characterized in that, Before obtaining the trajectory point sequence of the target object within the first time period, the method further includes: A first operation was detected, which is used to view the activity of the target object during the first time period.

6. A trajectory generation device, characterized in that, include: The processing module is used to obtain the trajectory point sequence of the target object within the first time period; The processing module is further configured to generate trajectory description text based on the trajectory point sequence, wherein the trajectory description text is a description of the target object's activity during the first time period; The display module is also used to display the trajectory description text.

7. The apparatus according to claim 6, characterized in that, When generating trajectory description text based on the trajectory point sequence, the processing module is specifically used for: Key information is extracted from the trajectory point sequence to obtain trajectory key features, wherein the trajectory key features include: the location information of the target object in different sub-time periods in the first time period, and the location information of adjacent sub-time periods is different; Based on the key features of the trajectory, the trajectory description text is obtained.

8. The apparatus according to claim 7, characterized in that, The display module is also used for: The key features of the trajectory are shown.

9. The apparatus according to any one of claims 6-8, characterized in that, The key features of the trajectory also include the orientation or direction between the location information of adjacent sub-time periods.

10. The apparatus according to any one of claims 6-9, characterized in that, Before acquiring the trajectory point sequence of the target object within the first time period, the processing module is further configured to: A first operation was detected, which is used to view the activity of the target object during the first time period.

11. An electronic device, characterized in that, include: At least one memory for storing programs; At least one processor is configured to invoke a program stored in the memory to implement the method as described in any one of claims 1-5.

12. A computer storage medium, a computer-readable storage medium, characterized in that, It includes computer program instructions that, when executed by a computing device, cause the computing device to perform the method as described in any one of claims 1-5.

13. A computer program product containing instructions, characterized in that, When the instruction is executed by the computing device, the computing device performs the method as described in any one of claims 1-5.

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