Driving suggestion generation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a driving suggestion generation method and device, electronic equipment and a storage medium. The method comprises the steps that positioning information of a guided object is acquired; based on the positioning information, determining a peripheral object which will appear around the guided object in a future set time period; and generating a driving suggestion for guiding the driving behavior of the guided object based on the driving information of the guided object and the driving information of the surrounding objects. According to the technical scheme, the driving suggestions for guiding the guided object to change lanes, accelerate, decelerate, keep lanes and other driving behaviors can be generated, so that the guided object can be helped to select a better driving strategy, and the driving efficiency and / or safety of the guided object on a road are / is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of navigation technology, and in particular to a driving suggestion generating method, device, electronic device and storage medium. Background Art

[0002] As high-precision map data becomes more and more complete, navigation services are increasingly dependent on high-precision map data. For example, in a lane-level navigation scenario, after the road-level path planning is completed, a lane-level navigation route can be generated by combining the planned road-level navigation route and high-precision map data, and then a lane-level navigation service is provided to the navigated object based on the lane-level navigation route. However, the inventors of the present disclosure have found that the existing technology cannot assist users in choosing a better driving strategy when driving on the current road in a lane-level navigation scenario, and can only guide users to take the regular lanes and broadcast or display factors such as the timing of safe lane changes.

[0003] To this end, it is necessary to provide a solution that can guide users to choose a better driving strategy on the current road, and assist users to drive the vehicle more safely and efficiently on the current road. Summary of the invention

[0004] Embodiments of the present disclosure provide a driving advice generating method, device, electronic device, and storage medium.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for generating driving advice, which includes: Obtaining positioning information of the guided object; Based on the positioning information, determining surrounding objects that will appear around the guided object in a set period of time in the future; Based on the driving information of the guided object and the driving information of the surrounding objects, driving advice for guiding the driving behavior of the guided object is generated.

[0006] In a second aspect, an embodiment of the present invention provides a driving advice generating device, comprising: A first acquisition module is configured to acquire positioning information of the guided object; A first determining module is configured to determine, based on the positioning information, surrounding objects that will appear around the guided object in a future set period of time; The generating module is configured to generate driving suggestions for guiding the driving behavior of the guided object based on the driving information of the guided object and the driving information of the surrounding objects.

[0007] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in any one of the above aspects.

[0008] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium for storing computer instructions used by any of the above-mentioned devices, and when the computer instructions are executed by a processor, they are used to implement the method described in any of the above-mentioned aspects.

[0009] In a fifth aspect, an embodiment of the present disclosure provides a computer program product, which includes computer instructions, and when the computer instructions are executed by a processor, they are used to implement the method described in any of the above aspects.

[0010] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: In this embodiment, the cloud or server can obtain the positioning information of the guided object during the navigation process, and predict the surrounding objects that will appear around the guided object in a set period of time in the future based on the positioning information. Then, based on the driving information of the guided object and the surrounding objects, driving suggestions are generated to guide the guided object to change lanes, accelerate, decelerate, keep lanes, and other driving behaviors, thereby helping the guided object to choose a better driving strategy and improve the driving efficiency and / or safety of the guided object on the road.

[0011] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Other features, objectives and advantages of the present disclosure will become more apparent through the following detailed description of non-limiting embodiments in conjunction with the accompanying drawings. In the accompanying drawings: Figure 1 A flowchart of a driving advice generating method provided by an embodiment of the present disclosure is shown.

[0013] Figure 2 A schematic diagram showing the effect of a traffic scenario prediction model provided by an embodiment of the present disclosure is shown.

[0014] Figure 3 A structural block diagram of a driving advice generating device provided by an embodiment of the present disclosure is shown.

[0015] Figure 4 A structural block diagram of an electronic device provided by one embodiment of the present disclosure is shown.

[0016] Figure 5 It is a structural diagram of a computer system suitable for implementing the driving suggestion generating method provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.

[0018] In the present disclosure, it should be understood that terms such as "include" or "have" are intended to indicate the presence of features, numbers, steps, behaviors, components, parts, or a combination thereof disclosed in the specification, and do not exclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or a combination thereof exist or are added.

[0019] It should also be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] The user information (including but not limited to user device information such as location information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0021] The details of the embodiments of the present disclosure are described in detail below through specific examples.

[0022] Figure 1 A flow chart of a method for generating driving advice provided by an embodiment of the present disclosure is shown. Figure 1 As shown, the driving suggestion generating method comprises the following steps: In step S101, the positioning information of the guided object is obtained; In step S102, based on the positioning information, surrounding objects that will appear around the guided object within a set period of time in the future are determined; In step S103 , a driving suggestion for guiding the driving behavior of the guided object is generated based on the driving information of the guided object and the driving information of the surrounding objects.

[0023] In this embodiment, the driving advice generation method can be executed in the cloud, on a server, or on a computer device with corresponding computing capabilities. The guided object can be understood as a user who uses a terminal device for navigation. Since the user is usually in the same location as the terminal device, the user's positioning information is also the positioning information of the terminal device. The positioning information may include the lane positioning information and road positioning information of the guided object. The method of the disclosed embodiment can be implemented when the guided object is traveling on a road where lane positioning information can be obtained, such as when traveling on a highway.

[0024] The surrounding objects may be objects that will appear in the same lane or adjacent lane of the same road as the guided object in a future set period. For example, the surrounding objects may be objects that will appear in the same lane of the guided object within a set distance in front of the guided object in the future set period, or objects that are located in the adjacent lane of the lane where the guided object is located and whose front and rear distances with the guided object do not exceed the set distance. The cloud or server can use the driving information of any object on each road on the past path to predict the driving path of these objects, so as to determine the surrounding objects that will appear around the guided object in the future set period based on the predicted driving path. The future set period may be a pre-set period of time, such as a period of time within 1 minute. The driving information on the above-mentioned past path may include but is not limited to the driving information on the set number of path points passed within a period of time before the step of determining the surrounding objects that will appear around the guided object in the future set period of time based on the positioning information. The driving information includes but is not limited to information related to speed, information related to driving behavior, and / or information related to the road and lane.

[0025] The driving information of the guided object and the driving information of the surrounding objects may also include, but are not limited to, information related to speed, information related to driving behavior, and / or information related to the road and lane. Information related to speed may include, for example, the current speed, average speed, maximum speed, etc. of the object; information related to driving behavior may include, for example, the number of lane changes, acceleration, deceleration, and other behaviors during driving; information related to the road and lane may include positioning information of the road or lane, such as the road or lane where the object is currently located, and may also include basic attribute information of the road, such as road level, etc. Of course, driving information is not limited to the above-mentioned information, and may also include other information that can help predict the future driving conditions of the object, and the present disclosure does not impose specific restrictions on this.

[0026] In some embodiments, the driving information of the guided object and the driving information of the surrounding objects can be used as input data of a pre-trained model, and the model finally outputs driving suggestions. In some embodiments, the model can adopt a Transformer Encoder architecture, whose input is the driving information of the guided object and the driving information of the surrounding objects at the current moment, and the output is the driving suggestions after a set period of time in the future.

[0027] Driving suggestions may be sent from the cloud or server to the guided object, and are suggestions for obtaining better driving benefits by guiding the guided object's driving behavior during the navigation process. Driving behaviors may include, but are not limited to: changing lanes, accelerating, decelerating, and / or keeping the current lane, etc. Driving suggestions may include information characterizing the above driving behaviors and related information. The relevant information may include, for example, corresponding driving benefits. For example, if the driving behavior included in the driving suggestion is lane changing, then the relevant information may include additional driving benefits that can be brought to the guided object after the lane change, such as the speed benefit amplitude value or the safety benefit amplitude value that can be improved; for another example, when the driving behavior includes acceleration or deceleration, then the relevant information may include the maximum speed to which it is accelerated or decelerated and the corresponding driving benefits, such as speed benefits, safety benefits, etc.

[0028] In this embodiment, the cloud or server can obtain the positioning information of the guided object during the navigation process, and predict the surrounding objects that will appear around the guided object in a set period of time in the future based on the positioning information. Then, based on the driving information of the guided object and the surrounding objects, driving suggestions are generated to guide the guided object to change lanes, accelerate, decelerate, keep lanes, and other driving behaviors, thereby helping the guided object to choose a better driving strategy and improve the driving efficiency and / or safety of the guided object on the road.

[0029] In an optional implementation of this embodiment, step S102, i.e., the step of determining surrounding objects that will appear around the guided object in a set period of time in the future based on the positioning information, can be implemented as follows: Acquiring driving information of the guided object during a first past set period of time; Based on the driving information, predicting a first predicted driving path of the guided object in a future set period of time; The surrounding objects are determined based on the first predicted driving path.

[0030] In this optional implementation, the driving information of the first past set period may be the historical driving information of the guided object, and the first past set period may be a time period of minutes. As described above, the driving information may include information related to speed, information related to driving behavior, and / or information related to the road and lane. Based on the driving information, the first predicted driving path of the guided object in the future set period can be predicted, and the first predicted driving path may include information such as the path points to which the guided object will travel in the future set period and the road and / or lane where the path points are located. For example, the average speed, maximum speed and lane information of the guided object at multiple path points before the current position point in the first past set period can be obtained, and the driving behavior of the guided object on the same lane of the current road, such as lane change frequency, etc., is obtained, and then the path points that the guided object will pass through in the future set period and the road and / or lane where the path points are located are predicted, and then the objects that will appear on the road where the above path points are located in the future set period can be determined as surrounding objects.

[0031] In an optional implementation of this embodiment, the method may further include the following steps: Acquire driving information of the candidate object during a first past set period of time; Based on the driving information, predicting a second predicted driving path of the candidate object in a future set period of time; The step of determining the surrounding objects based on the first predicted driving path may be implemented as follows: The surrounding objects are determined from the candidate objects based on the first predicted driving path and the second predicted driving path.

[0032] In this optional implementation, the cloud or server can also use the same method as the guided object to predict the second predicted driving path of the candidate object in the future set period based on the driving information of the candidate object traveling on each road section in the first past set period, and then determine the surrounding objects that will appear around the guided object and travel in the same direction in the future set period based on the first predicted driving path of the guided object and the second predicted driving path of the above-mentioned candidate object. For example, the candidate object whose path point on the first predicted driving path is on the same road as the path point on the first predicted driving path of the guided object can be determined as the surrounding object. Similarly, the driving information of the candidate object in the first past set period can be the historical driving information of the candidate object, and the first past set period can be a time period of minutes. The candidate object can be all or part of the objects on all or part of the road sections.

[0033] In an optional implementation, the cloud and / or the server can obtain the positioning information of some or all objects (including guided objects and candidate objects) on each road section in real time, and predict the first predicted driving path and the second predicted driving path of the guided object and the candidate object in the first past set period of time, and when generating driving suggestions for the guided object, the first predicted driving path and the second predicted driving path can be directly obtained from the results. Therefore, the execution order of the steps of predicting the first predicted driving path of the guided object and the steps of predicting the second predicted driving path of the candidate object is not specific.

[0034] In an optional implementation of this embodiment, the method may further include the following steps: speed comparison information for determining whether the lane speed of the adjacent lane is faster than the lane where the guided object is located based on the current speed of the guided object and the lane speed of the adjacent lane to the lane where the guided object is located; Determine whether there is a vehicle ahead of the lane where the guided object is located with a slower speed; Step S103, i.e., the step of generating driving suggestions for guiding the driving behavior of the guided object based on the driving information of the guided object and the driving information of the surrounding objects, can be implemented as follows: The driving advice is generated based on the driving information of the guided object, the driving information of the surrounding objects, the speed comparison information, and the front vehicle pressure information.

[0035] In this optional implementation, the lane speed of the adjacent lane is the average speed of the objects traveling in the adjacent lane, and the lane speed of the lane where the guided object is located is the average speed of the objects traveling in the lane. The purpose of determining the speed comparison information is to determine whether the guided object can speed up if it changes lanes to the adjacent lane.

[0036] In some embodiments, in order to make the speed comparison information more accurate, the speed interval from the lowest speed to the highest speed of all or part of the objects traveling in the adjacent lane and the lane where the guided object is located can be evenly divided into multiple speed intervals, and each speed interval corresponds to a speed quantile. Afterwards, the target speed quantile at which the current speed of the guided object is located in the lane where the guided object is located can be determined. For example, the speed of the guided object is between 50-75 percent of the speed interval from the lowest speed to the highest speed of all or part of the objects in the lane where the guided object is located. If the average speed between 50-75 percent of the speed interval from the lowest speed to the highest speed of all or part of the objects in the adjacent lane is greater than the average speed between 50-75 percent of the speed of the lane where the guided object is located, it can be considered that the adjacent lane is faster than the lane speed of the guided object, and the speed comparison information may include the adjacent lane and the average speed of the adjacent lane in the target speed quantile interval, etc.

[0037] The front vehicle information may include but is not limited to whether there is an object in the lane where the guided object is located and in front of the guided object whose speed is slower than that of the guided object. If there is, the front vehicle information may include the current speed of the slower object and / or the distance to the guided object. Whether there is an object with a slower speed can be determined by using the positioning information obtained by the cloud or the server from the objects traveling on each road section, or by analyzing the driving information of the guided object.

[0038] In some embodiments, the speed comparison information and the vehicle pressure information ahead can be predicted based on the current speed comparison information and the vehicle pressure information ahead, and then combined with the driving information of the guided object and the driving information of the surrounding objects, a more accurate driving suggestion can be obtained. It should be noted that the steps of determining the speed comparison information and the vehicle pressure information ahead are executed in no particular order, and are determined according to the actual implementation needs, and the present disclosure does not make any specific restrictions on this.

[0039] It is understandable that the driving information of the guided object, the driving information of the surrounding objects, the speed comparison information and the information of the vehicle ahead can be used as the model input by training the corresponding model, and the model can finally output the driving suggestions. In some embodiments, the model can adopt the Transformer Encoder architecture, whose input is the above information at the current moment, and the output is the driving suggestions after a set period of time in the future.

[0040] Figure 2 A schematic diagram showing the effect of a traffic scene prediction model provided by an embodiment of the present disclosure is shown. Figure 2As shown in the figure, at time Tk, it can be calculated that the lane speed of the black car in the top lane is higher than the lane speed of the ego vehicle, which is the lane where the guided object is located. The faster lane is shown in gray shadow in the figure. After k seconds, that is, at time T, the black car in the top lane has moved forward for a longer distance, while the ego vehicle has moved forward for a shorter distance. This is because there is a vehicle (black truck) in front of the ego vehicle, and its forward speed is slower than that of the black car in the faster lane. According to the above scenario, at time Tk, based on the faster lane and whether there is a vehicle in front of the ego vehicle, it can be predicted that before time T, if the ego vehicle changes lanes to the faster lane, it can increase its speed by a certain amount compared to staying in the current lane.

[0041] In an optional implementation of this embodiment, the step of determining whether there is any vehicle ahead information of an object with a slower speed ahead of the lane where the guided object is located may be implemented in one or more combinations of the following: Determining the front vehicle pressing information based on the current speed of the object traveling in the lane where the guided object is located and located in front and the current speed of the guided object; The forward vehicle congestion information is determined based on the speed trajectory of the guided object and the traffic flow on the lane where the guided object is located.

[0042] In this optional implementation, for objects traveling on each road section, if the cloud or server can obtain positioning information, the speed of these objects can be determined. Therefore, the current speed of the object traveling in the lane where the guided object is located and in front of the guided object can be determined. If the current speed of the object is less than the current speed of the guided object, it can be determined that there is a slower object in front of the guided object, and then the front vehicle pressure information can be generated based on the slower object. The front vehicle pressure information may include the current speed of the slower object and / or the distance to the guided object, etc. If the current speed of the object is greater than or equal to the current speed of the guided object, the front vehicle pressure information may be empty.

[0043] It is understandable that the cloud or server may not be able to obtain the positioning information of all objects on each road section. In order to improve the prediction accuracy, the embodiment of the present disclosure also determines whether there is a slower object in front of the lane of the guided object based on the speed trajectory of the guided object and the traffic flow of the lane where the guided object is located, and whether the existence of a slower object generates front vehicle congestion information.

[0044] The speed trajectory of the guided object can be understood as information about how the speed changes over time in a period of time before the current moment. Based on the speed trajectory, the speed values ​​at different time sampling points in a period of time before the current moment can be known.

[0045] Based on the speed trajectory of the guided object and the traffic flow in the lane where the guided object is located, it can be inferred whether there is a slower object in front of the guided object. For example, a model can be trained to predict the information of the vehicle ahead, with the speed trajectory of the guided object and the corresponding traffic flow as input, and the model predicts and outputs the information of the vehicle ahead, such as whether there is a slower object in front of the guided object and the distance between the slower object and the guided object.

[0046] It is understandable that if the guided object decelerates and the deceleration is rapid, it can be inferred that there is a slower object in front of the guided object and that the object is closer to the guided object. However, if the guided object decelerates but the deceleration is slow, if the traffic flow in the lane where the guided object is located is small, it can be inferred that the deceleration of the guided object has little to do with the slower vehicle in front, while if the traffic flow is large, it can be inferred that the deceleration of the guided object has a greater relationship with the slower vehicle in front.

[0047] In some embodiments, the Transformer Encoder architecture can be used to implement a prediction model for vehicle congestion information ahead. The model can compress information on the speed trajectory to determine the degree of deceleration (faster or slower), and use the relationship between traffic flow and speed to determine whether the current speed corresponds to a large or small flow. Then, based on the degree of deceleration and whether the current speed corresponds to a large or small flow, the vehicle congestion information ahead is output.

[0048] In an optional implementation of this embodiment, the method may further include the following steps: Acquire a first predicted driving path of the guided object and a second predicted driving path of the surrounding objects in the future set time period; Acquiring environmental information on the first predicted driving path and the second predicted driving path; Step S103, i.e., the step of generating driving suggestions for guiding the driving behavior of the guided object based on the driving information of the guided object and the driving information of the surrounding objects, can be implemented as follows: The driving advice is generated based on the driving information of the guided object, the driving information of the surrounding objects, the first predicted driving path, the second predicted driving path, and the environmental information.

[0049] In this optional implementation, if the first predicted driving path and the second predicted driving path of the guided object in the future set time period have been predicted, the first predicted driving path and the second predicted driving path can be directly obtained; if they have not been predicted, the first predicted driving path and the second predicted driving path are predicted in the manner described above.

[0050] The environmental information of the first predicted driving path and the environmental information of the second predicted driving path may include road safety environmental information involved in the predicted driving path, such as construction information, accident information, abnormal road occupation information, slow vehicles, sudden braking vehicles, fast vehicles behind, etc.

[0051] When generating driving suggestions, in addition to considering the driving information of the guided object and the surrounding objects themselves, the first predicted driving path, the second predicted driving path, and the environmental information may be integrated, thereby further improving the accuracy of the driving suggestions. In some embodiments, the driving information of the guided object, the driving information of the surrounding objects, the first predicted driving path, the second predicted driving path, and the environmental information may be used as inputs to a pre-trained model, and the model outputs the driving suggestions.

[0052] It is understandable that the first predicted driving path and the second predicted driving path can be combined with the speed comparison information and the front vehicle pressure information in the above-mentioned embodiment, and then with the driving information of the guided object and the surrounding objects to jointly decide to improve the driving suggestions for the guided object. In some embodiments, the driving information of the guided object, the driving information of the surrounding objects, the speed comparison information, the front vehicle pressure information, the first predicted driving path, the second predicted driving path and the environmental information can be used as the input of the pre-trained model, and the model outputs the driving suggestions.

[0053] In an optional implementation of this embodiment, the method may further include the following steps: Determining first driving portrait information of the guided object and second driving portrait information of the surrounding objects; Step S103, i.e., the step of generating a driving suggestion for guiding the driving behavior of the guided object based on the driving information of the guided object and the driving information of the surrounding objects, comprises: The driving advice is generated based on the driving information of the guided object, the driving information of the surrounding objects, the first driving portrait information, and the second driving portrait information.

[0054] In this optional implementation, the first driving portrait information of the guided object and the second driving portrait information of the surrounding objects can be used to describe the driving behavior characteristics and personality characteristics of the guided object and the surrounding objects, such as focusing on safe driving, tending to drive fast, and being more emotional.

[0055] When generating driving suggestions, in addition to considering the driving information of the guided object and the surrounding objects themselves, the first driving portrait information and the second driving portrait information may also be integrated, thereby further improving the accuracy of the driving suggestions. In some embodiments, the driving information of the guided object, the driving information of the surrounding objects, the first driving portrait information, and the second driving portrait information may be used as inputs to a pre-trained model, and the model outputs driving suggestions.

[0056] It is understandable that the first driving portrait information and the second driving portrait information can also be integrated with the first predicted driving path and the second predicted driving path environmental information in the above-mentioned embodiment, so that the generated driving suggestions are more accurate. In some embodiments, the driving information of the guided object, the driving information of the surrounding objects, the speed comparison information, the front vehicle pressure information, the first predicted driving path, the second predicted driving path, the environmental information, the first driving portrait information and the second driving portrait information can be used as the input of the pre-trained model, and the model outputs the driving suggestions.

[0057] In an optional implementation of this embodiment, the step of determining the first driving portrait information of the guided object and the second driving portrait information of the surrounding objects may be implemented as follows: Determining historical driving information of the guided object and the surrounding objects and real-time driving information of a second past set period of time; Based on the historical driving information and the real-time driving information, the first driving portrait information and the second driving portrait information are determined.

[0058] In this optional implementation, the historical driving information of the guided object and the surrounding objects may include but is not limited to: historical driving mileage, historical lane change information, and the speed percentile of the historical average driving speed on the target type road; the historical lane change information may include lane change frequency, the duration between each two lane change behaviors, etc.; the speed percentile is the speed range in which the historical average driving speed of the guided object and the surrounding objects on the target type road is compared with other objects, for example, the historical average driving speed from the lowest speed to the highest speed of all objects on the target type road is evenly divided into 10 speed ranges, each speed range corresponds to a speed percentile, for example, the speed range of 10% to 20% corresponds to the 20th speed percentile; the speed fraction corresponding to the speed range in which the historical average driving speed of the guided object and the surrounding objects is located is the speed percentile of the guided object and the surrounding objects; the target type may be the road type of the road where the guided object and the surrounding objects are located.

[0059] The real-time driving information of the guided object and the surrounding objects may include but is not limited to: the current speed, the number of lane changes in the second past set time period, and the speed percentile of the current speed on the current road; the current road is the road where the guided object and the surrounding objects are located, and the speed percentile corresponds to the speed range in which the current speed of the guided object and the surrounding objects is located compared with other objects on the current road.

[0060] The following are embodiments of the apparatus of the present disclosure, which can be used to execute embodiments of the method of the present disclosure.

[0061] Figure 3 FIG. 1 is a block diagram showing a driving advice generating device provided by an embodiment of the present disclosure. The device can be implemented as part or all of an electronic device through software, hardware, or a combination of both. Figure 3 As shown, the driving suggestion generating device includes: A first acquisition module 301 is configured to acquire positioning information of a guided object; A first determining module 302 is configured to determine, based on the positioning information, surrounding objects that will appear around the guided object in a set period of time in the future; The generating module 303 is configured to generate driving suggestions for guiding the driving behavior of the guided object based on the driving information of the guided object and the driving information of the surrounding objects.

[0062] In this embodiment, the driving advice generation module can be executed on the cloud, server or computer device with corresponding computing power. The guided object can be understood as a user who uses a terminal device for navigation. Since the user is usually in the same location as the terminal device, the user's positioning information is also the positioning information of the terminal device. The positioning information may include the lane positioning information and road positioning information of the guided object. The module of the disclosed embodiment can be implemented when the guided object is traveling on a road where lane positioning information can be obtained, such as when traveling on a highway.

[0063] The surrounding objects may be objects that will appear in the same lane or adjacent lane of the same road as the guided object in a future set period. For example, the surrounding objects may be objects that will appear in the same lane of the guided object within a set distance in front of the guided object in the future set period, or objects that are located in the adjacent lane of the lane where the guided object is located and whose front and rear distances with the guided object do not exceed the set distance. The cloud or server can use the driving information of any object on each road on the past path to predict the driving path of these objects, so as to determine the surrounding objects that will appear around the guided object in the future set period based on the predicted driving path. The future set period may be a pre-set period of time, such as a period of time within 1 minute. The driving information on the above-mentioned past path may include but is not limited to the driving information on the set number of path points passed within a period of time before the step of determining the surrounding objects that will appear around the guided object in the future set period of time based on the positioning information. The driving information includes but is not limited to information related to speed, information related to driving behavior, and / or information related to the road and lane.

[0064] The driving information of the guided object and the driving information of the surrounding objects may also include, but are not limited to, information related to speed, information related to driving behavior, and / or information related to the road and lane. Information related to speed may include, for example, the current speed, average speed, maximum speed, etc. of the object; information related to driving behavior may include, for example, the number of lane changes, acceleration, deceleration, and other behaviors during driving; information related to the road and lane may include positioning information of the road or lane, such as the road or lane where the object is currently located, and may also include basic attribute information of the road, such as road level, etc. Of course, driving information is not limited to the above-mentioned information, and may also include other information that can help predict the future driving conditions of the object, and the present disclosure does not impose specific restrictions on this.

[0065] In some embodiments, the driving information of the guided object and the driving information of the surrounding objects can be used as input data of a pre-trained model, and the model finally outputs driving suggestions. In some embodiments, the model can adopt a Transformer Encoder architecture, whose input is the driving information of the guided object and the driving information of the surrounding objects at the current moment, and the output is the driving suggestions after a set period of time in the future.

[0066] Driving suggestions may be sent from the cloud or server to the guided object, and are suggestions for obtaining better driving benefits by guiding the guided object's driving behavior during the navigation process. Driving behaviors may include, but are not limited to: changing lanes, accelerating, decelerating, and / or keeping the current lane, etc. Driving suggestions may include information characterizing the above driving behaviors and related information. The relevant information may include, for example, corresponding driving benefits. For example, if the driving behavior included in the driving suggestion is lane changing, then the relevant information may include additional driving benefits that can be brought to the guided object after the lane change, such as the speed benefit amplitude value or the safety benefit amplitude value that can be improved; for another example, when the driving behavior includes acceleration or deceleration, then the relevant information may include the maximum speed to which it is accelerated or decelerated and the corresponding driving benefits, such as speed benefits, safety benefits, etc.

[0067] In this embodiment, the cloud or server can obtain the positioning information of the guided object during the navigation process, and predict the surrounding objects that will appear around the guided object in a set period of time in the future based on the positioning information. Then, based on the driving information of the guided object and the surrounding objects, driving suggestions are generated to guide the guided object to change lanes, accelerate, decelerate, keep lanes, and other driving behaviors, thereby helping the guided object to choose a better driving strategy and improve the driving efficiency and / or safety of the guided object on the road.

[0068] In an optional implementation of this embodiment, the first determining module may be implemented as follows: Acquiring driving information of the guided object during a first past set period of time; Based on the driving information, predicting a first predicted driving path of the guided object in a future set period of time; The surrounding objects are determined based on the first predicted driving path.

[0069] In this optional implementation, the driving information of the first past set period may be the historical driving information of the guided object, and the first past set period may be a time period of minutes. As described above, the driving information may include information related to speed, information related to driving behavior, and / or information related to the road and lane. Based on the driving information, the first predicted driving path of the guided object in the future set period can be predicted, and the first predicted driving path may include information such as the path points to which the guided object will travel in the future set period and the road and / or lane where the path points are located. For example, the average speed, maximum speed and lane information of the guided object at multiple path points before the current position point in the first past set period can be obtained, and the driving behavior of the guided object on the same lane of the current road, such as lane change frequency, etc., is obtained, and then the path points that the guided object will pass through in the future set period and the road and / or lane where the path points are located are predicted, and then the objects that will appear on the road where the above path points are located in the future set period can be determined as surrounding objects.

[0070] In an optional implementation of this embodiment, the device may further include: A second acquisition module is configured to acquire driving information of the candidate object in a first past set period of time; A prediction module, configured to predict a second predicted driving path of the candidate object in a future set period based on the driving information; The second determination module is configured to determine the step of the surrounding objects based on the first predicted driving path, and can be implemented in the following manner: The third determination module is configured to determine the surrounding objects from the candidate objects based on the first predicted driving path and the second predicted driving path.

[0071] In this optional implementation, the cloud or server can also use the same module as the guided object to predict the second predicted driving path of the candidate object in the future set period based on the driving information of the candidate object traveling on each road section in the first past set period, and then determine the surrounding objects that will appear around the guided object and travel in the same direction in the future set period based on the first predicted driving path of the guided object and the second predicted driving path of the above-mentioned candidate object. For example, the candidate object whose path point on the first predicted driving path is on the same road as the path point on the first predicted driving path of the guided object can be determined as the surrounding object. Similarly, the driving information of the candidate object in the first past set period can be the historical driving information of the candidate object, and the first past set period can be a time period of minutes. The candidate object can be all or part of the objects on all or part of the road sections.

[0072] In an optional implementation, the cloud and / or the server can obtain the positioning information of some or all objects (including guided objects and candidate objects) on each road section in real time, and predict the first predicted driving path and the second predicted driving path of the guided object and the candidate object in the first past set period of time, and when generating driving suggestions for the guided object, the first predicted driving path and the second predicted driving path can be directly obtained from the results. Therefore, the execution order of the steps of predicting the first predicted driving path of the guided object and the steps of predicting the second predicted driving path of the candidate object is not specific.

[0073] In an optional implementation of this embodiment, the device may further include: a fourth determination module configured to determine, based on the current speed of the guided object and the lane speed of an adjacent lane to the lane where the guided object is located, speed comparison information of whether the lane speed of the adjacent lane is faster than the lane where the guided object is located; A fifth determination module is configured to determine whether there is a front vehicle pressure information of an object with a slower speed ahead of the lane where the guided object is located; The generation module can be implemented as follows: The driving advice is generated based on the driving information of the guided object, the driving information of the surrounding objects, the speed comparison information, and the front vehicle pressure information.

[0074] In this optional implementation, the lane speed of the adjacent lane is the average speed of the objects traveling in the adjacent lane, and the lane speed of the lane where the guided object is located is the average speed of the objects traveling in the lane. The purpose of determining the speed comparison information is to determine whether the guided object can speed up if it changes lanes to the adjacent lane.

[0075] In some embodiments, in order to make the speed comparison information more accurate, the speed interval from the lowest speed to the highest speed of all or part of the objects traveling in the adjacent lane and the lane where the guided object is located can be evenly divided into multiple speed intervals, and each speed interval corresponds to a speed quantile. Afterwards, the target speed quantile at which the current speed of the guided object is located in the lane where the guided object is located can be determined. For example, the speed of the guided object is between 50-75 percent of the speed interval from the lowest speed to the highest speed of all or part of the objects in the lane where the guided object is located. If the average speed between 50-75 percent of the speed interval from the lowest speed to the highest speed of all or part of the objects in the adjacent lane is greater than the average speed between 50-75 percent of the speed of the lane where the guided object is located, it can be considered that the adjacent lane is faster than the lane speed of the guided object, and the speed comparison information may include the adjacent lane and the average speed of the adjacent lane in the target speed quantile interval, etc.

[0076] The front vehicle information may include but is not limited to whether there is an object in the lane where the guided object is located and in front of the guided object whose speed is slower than that of the guided object. If there is, the front vehicle information may include the current speed of the slower object and / or the distance to the guided object. Whether there is an object with a slower speed can be determined by using the positioning information obtained by the cloud or the server from the objects traveling on each road section, or by analyzing the driving information of the guided object.

[0077] In some embodiments, the speed comparison information and the front vehicle pressure information at the current moment can be used to predict the speed comparison information and the front vehicle pressure information in the future set period, and then combined with the driving information of the guided object and the driving information of the surrounding objects, a more accurate driving suggestion can be obtained. It should be noted that the fourth determination module for determining the speed comparison information and the fifth determination module for determining the front vehicle pressure information are not in order, and are determined according to the actual implementation needs, and the present disclosure does not make specific restrictions on this.

[0078] It is understandable that the driving information of the guided object, the driving information of the surrounding objects, the speed comparison information and the information of the vehicle ahead can be used as the model input by training the corresponding model, and the model can finally output the driving suggestions. In some embodiments, the model can adopt the Transformer Encoder architecture, whose input is the above information at the current moment, and the output is the driving suggestions after a set period of time in the future.

[0079] In an optional implementation of this embodiment, the determining whether there is a vehicle ahead pressure information of an object with a slower speed ahead of the lane where the guided object is located may be implemented in one or more of the following combinations: Determining the front vehicle pressing information based on the current speed of the object traveling in the lane where the guided object is located and located in front and the current speed of the guided object; The forward vehicle congestion information is determined based on the speed trajectory of the guided object and the traffic flow on the lane where the guided object is located.

[0080] In this optional implementation, for objects traveling on each road section, if the cloud or server can obtain positioning information, the speed of these objects can be determined. Therefore, the current speed of the object traveling in the lane where the guided object is located and in front of the guided object can be determined. If the current speed of the object is less than the current speed of the guided object, it can be determined that there is a slower object in front of the guided object, and then the front vehicle pressure information can be generated based on the slower object. The front vehicle pressure information may include the current speed of the slower object and / or the distance to the guided object, etc. If the current speed of the object is greater than or equal to the current speed of the guided object, the front vehicle pressure information may be empty.

[0081] It is understandable that the cloud or server may not be able to obtain the positioning information of all objects on each road section. In order to improve the prediction accuracy, the embodiment of the present disclosure also determines whether there is a slower object in front of the lane of the guided object based on the speed trajectory of the guided object and the traffic flow of the lane where the guided object is located, and whether the existence of a slower object generates front vehicle congestion information.

[0082] The speed trajectory of the guided object can be understood as information about how the speed changes over time in a period of time before the current moment. Based on the speed trajectory, the speed values ​​at different time sampling points in a period of time before the current moment can be known.

[0083] Based on the speed trajectory of the guided object and the traffic flow in the lane where the guided object is located, it can be inferred whether there is a slower object in front of the guided object. For example, a model can be trained to predict the information of the vehicle ahead, with the speed trajectory of the guided object and the corresponding traffic flow as input, and the model predicts and outputs the information of the vehicle ahead, such as whether there is a slower object in front of the guided object and the distance between the slower object and the guided object.

[0084] It is understandable that if the guided object decelerates and the deceleration is rapid, it can be inferred that there is a slower object in front of the guided object and that the object is closer to the guided object. However, if the guided object decelerates but the deceleration is slow, if the traffic flow in the lane where the guided object is located is small, it can be inferred that the deceleration of the guided object has little to do with the slower vehicle in front, while if the traffic flow is large, it can be inferred that the deceleration of the guided object has a greater relationship with the slower vehicle in front.

[0085] In some embodiments, the Transformer Encoder architecture can be used to implement a prediction model for vehicle congestion information ahead. The model can compress information on the speed trajectory to determine the degree of deceleration (faster or slower), and use the relationship between traffic flow and speed to determine whether the current speed corresponds to a large or small flow. Then, based on the degree of deceleration and whether the current speed corresponds to a large or small flow, the vehicle congestion information ahead is output.

[0086] In an optional implementation of this embodiment, the device may further include: A third acquisition module is configured to acquire a first predicted driving path of the guided object and a second predicted driving path of the surrounding objects in the future set time period; a fourth acquisition module, configured to acquire environmental information on the first predicted driving path and the second predicted driving path; The generation module can be implemented as follows: The driving advice is generated based on the driving information of the guided object, the driving information of the surrounding objects, the first predicted driving path, the second predicted driving path, and the environmental information.

[0087] In this optional implementation, if the first predicted driving path and the second predicted driving path of the guided object in the future set time period have been predicted, the first predicted driving path and the second predicted driving path can be directly obtained; if they have not been predicted, the first predicted driving path and the second predicted driving path are predicted in the manner described above.

[0088] The environmental information of the first predicted driving path and the environmental information of the second predicted driving path may include road safety environmental information involved in the predicted driving path, such as construction information, accident information, abnormal road occupation information, slow vehicles, sudden braking vehicles, fast vehicles behind, etc.

[0089] When generating driving suggestions, in addition to considering the driving information of the guided object and the surrounding objects themselves, the first predicted driving path, the second predicted driving path, and the environmental information may be integrated, thereby further improving the accuracy of the driving suggestions. In some embodiments, the driving information of the guided object, the driving information of the surrounding objects, the first predicted driving path, the second predicted driving path, and the environmental information may be used as inputs to a pre-trained model, and the model outputs the driving suggestions.

[0090] It is understandable that the first predicted driving path and the second predicted driving path can be combined with the speed comparison information and the front vehicle pressure information in the above-mentioned embodiment, and then with the driving information of the guided object and the surrounding objects to jointly decide to improve the driving suggestions for the guided object. In some embodiments, the driving information of the guided object, the driving information of the surrounding objects, the speed comparison information, the front vehicle pressure information, the first predicted driving path, the second predicted driving path and the environmental information can be used as the input of the pre-trained model, and the model outputs the driving suggestions.

[0091] In an optional implementation of this embodiment, the device may further include: A sixth determining module is configured to determine the first driving portrait information of the guided object and the second driving portrait information of the surrounding objects; The generating module comprises: The driving advice is generated based on the driving information of the guided object, the driving information of the surrounding objects, the first driving portrait information, and the second driving portrait information.

[0092] In this optional implementation, the first driving portrait information of the guided object and the second driving portrait information of the surrounding objects can be used to describe the driving behavior characteristics and personality characteristics of the guided object and the surrounding objects, such as focusing on safe driving, tending to drive fast, and being more emotional.

[0093] When generating driving suggestions, in addition to considering the driving information of the guided object and the surrounding objects themselves, the first driving portrait information and the second driving portrait information may also be integrated, thereby further improving the accuracy of the driving suggestions. In some embodiments, the driving information of the guided object, the driving information of the surrounding objects, the first driving portrait information, and the second driving portrait information may be used as inputs to a pre-trained model, and the model outputs driving suggestions.

[0094] It is understandable that the first driving portrait information and the second driving portrait information can also be integrated with the first predicted driving path and the second predicted driving path environmental information in the above-mentioned embodiment, so that the generated driving suggestions are more accurate. In some embodiments, the driving information of the guided object, the driving information of the surrounding objects, the speed comparison information, the front vehicle pressure information, the first predicted driving path, the second predicted driving path, the environmental information, the first driving portrait information and the second driving portrait information can be used as the input of the pre-trained model, and the model outputs the driving suggestions.

[0095] In an optional implementation of this embodiment, the determining of the first driving portrait information of the guided object and the second driving portrait information of the surrounding objects may be implemented as follows: Determining historical driving information of the guided object and the surrounding objects and real-time driving information of a second past set period of time; Based on the historical driving information and the real-time driving information, the first driving portrait information and the second driving portrait information are determined.

[0096] In this optional implementation, the historical driving information of the guided object and the surrounding objects may include but is not limited to: historical driving mileage, historical lane change information, and the speed percentile of the historical average driving speed on the target type road; the historical lane change information may include lane change frequency, the duration between each two lane change behaviors, etc.; the speed percentile is the speed range in which the historical average driving speed of the guided object and the surrounding objects on the target type road is compared with other objects, for example, the historical average driving speed from the lowest speed to the highest speed of all objects on the target type road is evenly divided into 10 speed ranges, each speed range corresponds to a speed percentile, for example, the speed range of 10% to 20% corresponds to the 20th speed percentile; the speed fraction corresponding to the speed range in which the historical average driving speed of the guided object and the surrounding objects is located is the speed percentile of the guided object and the surrounding objects; the target type may be the road type of the road where the guided object and the surrounding objects are located.

[0097] The real-time driving information of the guided object and the surrounding objects may include but is not limited to: the current speed, the number of lane changes in the second past set time period, and the speed percentile of the current speed on the current road; the current road is the road where the guided object and the surrounding objects are located, and the speed percentile corresponds to the speed range in which the current speed of the guided object and the surrounding objects is located compared with other objects on the current road.

[0098] The functions implemented by the above-mentioned device can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-mentioned functions.

[0099] In one possible design, the structure of the above-mentioned device includes a memory and a processor, the memory is used to store one or more computer instructions that support the above-mentioned device to execute the above-mentioned corresponding device, and the processor is configured to execute the computer instructions stored in the memory. The above-mentioned device may also include a communication interface for the above-mentioned device to communicate with other devices or communication networks.

[0100] The present disclosure also discloses an electronic device, Figure 4 A structural block diagram of an electronic device provided by an embodiment of the present disclosure is shown as follows: Figure 4 As shown, the electronic device 400 includes a memory 401 and a processor 402; wherein, The memory 401 is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 402 to implement the above method steps.

[0101] Figure 5 It is a structural diagram of a computer system suitable for implementing the driving suggestion generating method provided in one embodiment of the present disclosure.

[0102] like Figure 5 As shown, the computer system 500 includes a processing unit 501, which can be implemented as a processing unit such as a CPU, a GPU, an FPGA, and an NPU. The processing unit 501 can perform various processes in the embodiments of any of the above methods of the present disclosure according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 to a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the computer system 500 are also stored. The processing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0103] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0104] In particular, according to an embodiment of the present disclosure, any method in the above referenced embodiment of the present disclosure may be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program tangibly contained on a machine-readable medium, and the computer program includes a program code for executing any method in the embodiment of the present disclosure. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 509, and / or installed from a removable medium 511.

[0105] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0106] The units or modules involved in the embodiments described in the present disclosure may be implemented by software or hardware. The units or modules described may also be set in a processor, and the names of these units or modules do not constitute limitations on the units or modules themselves in some cases.

[0107] As another aspect, the present disclosure further provides a computer-readable storage medium, which may be a computer-readable storage medium included in the device described in the above embodiment; or a computer-readable storage medium that exists independently and is not assembled into the device. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute the method described in the present disclosure.

[0108] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other.

Claims

1. A method for generating driving advice, wherein: include: Obtaining positioning information of the guided object; Based on the positioning information, determining surrounding objects that will appear around the guided object in a set period of time in the future; Based on the driving information of the guided object and the driving information of the surrounding objects, driving advice for guiding the driving behavior of the guided object is generated.

2. The method according to claim 1, wherein: Determining, based on the positioning information, surrounding objects that will appear around the guided object in a future set period of time includes: Acquiring driving information of the guided object during a first past set period of time; Based on the driving information, predicting a first predicted driving path of the guided object in a future set period of time; The surrounding objects are determined based on the first predicted driving path.

3. The method according to claim 2, wherein: The method further comprises: Acquire driving information of the candidate object during a first past set period of time; Based on the driving information, predicting a future predicted driving path of the candidate object in a future set period of time; Determining the surrounding objects based on the first predicted driving path includes: The surrounding objects are determined from the candidate objects based on the first predicted driving path and the second predicted driving path.

4. The method according to any one of claims 1 to 3, wherein: The method further comprises: speed comparison information for determining whether the lane speed of the adjacent lane is faster than the lane where the guided object is located based on the current speed of the guided object and the lane speed of the adjacent lane to the lane where the guided object is located; Determine whether there is a vehicle ahead of the lane where the guided object is located with a slower speed; Generating a driving suggestion for guiding a driving behavior of the guided object based on the driving information of the guided object and the driving information of the surrounding objects, including: The driving advice is generated based on the driving information of the guided object, the driving information of the surrounding objects, the speed comparison information, and the front vehicle pressure information.

5. The method according to claim 4, wherein: Determining whether there is a vehicle ahead of the lane where the guided object is located with a slower speed includes one or more of the following combinations: Determining the front vehicle pressing information based on the current speed of the object traveling in the lane where the guided object is located and located in front and the current speed of the guided object; The forward vehicle congestion information is determined based on the speed trajectory of the guided object and the traffic flow on the lane where the guided object is located.

6. The method according to any one of claims 1 to 3, wherein: The method further comprises: Obtaining a first predicted driving path of the guided object and a second predicted driving path of the surrounding objects in the future set time period; Acquiring environmental information on the first predicted driving path and the second predicted driving path; Generating a driving suggestion for guiding a driving behavior of the guided object based on the driving information of the guided object and the driving information of the surrounding objects, including: The driving advice is generated based on the driving information of the guided object, the driving information of the surrounding objects, the first predicted driving path, the second predicted driving path, and the environmental information.

7. The method according to any one of claims 1 to 3, wherein: The method further comprises: Determining first driving portrait information of the guided object and second driving portrait information of the surrounding objects; Generating a driving suggestion for guiding a driving behavior of the guided object based on the driving information of the guided object and the driving information of the surrounding objects, including: The driving advice is generated based on the driving information of the guided object, the driving information of the surrounding objects, the first driving portrait information, and the second driving portrait information.

8. The method according to claim 5, wherein: Determining the first driving portrait information of the guided object and the second driving portrait information of the surrounding objects includes: Determining historical driving information of the guided object and the surrounding objects and real-time driving information of a second past set period of time; Based on the historical driving information and the real-time driving information, the first driving portrait information and the second driving portrait information are determined.

9. A driving advice generating device, wherein: include: A first acquisition module is configured to acquire positioning information of the guided object; A first determining module is configured to determine, based on the positioning information, surrounding objects that will appear around the guided object in a future set period of time; The generating module is configured to generate driving suggestions for guiding the driving behavior of the guided object based on the driving information of the guided object and the driving information of the surrounding objects.

10. An electronic device, wherein: The method comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having computer instructions stored thereon, wherein: When the computer instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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