CN115237440A

CN115237440A - Information processing method and device, electronic equipment and storage medium

Info

Publication number: CN115237440A
Application number: CN202210736814.6A
Authority: CN
Inventors: 曹杨
Original assignee: Suzhou Jianzhi Robot Technology Co ltd
Current assignee: Suzhou Jianzhi Robot Technology Co ltd
Priority date: 2022-06-27
Filing date: 2022-06-27
Publication date: 2022-10-25

Abstract

The embodiment of the invention provides an information processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring reference information of a plurality of users, wherein the reference information comprises behavior information, driving information and feedback information belonging to the same user, the behavior information comprises behavior information of the users in terminal equipment associated with the vehicle, the driving information comprises the driving information when the vehicle logs in a user account, and the feedback information comprises use feedback information of the vehicle by the users; pushing a software update package of an autonomous driving system of the vehicle to a plurality of users according to the reference information. Therefore, according to one embodiment of the invention, a software update package of the automatic driving system of the vehicle can be generated from a plurality of data dimensions and pushed to the user, so that the driving control strategy of the automatic driving system better meets the actual requirements of the user, the automatic driving experience of the user is further improved, and the application period of the user to the automatic driving function is shortened.

Description

Information processing method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of communications technologies, and in particular, to an information processing method and apparatus, an electronic device, and a storage medium.

Background

With the development of the automatic driving technology, the degree of liberation of driving tasks brought to users is higher and higher, but the level of cognition and confidence of the user market for automatic driving is far behind the development speed of automatic driving products, so that the common situation that the experience degree in the use of actual functions is poor and the user expectation is not met is caused.

In order to improve the purchase willingness and the use rate of the user after purchase on the automatic driving function product, technical scheme suppliers in a host factory and a supply chain make many attempts on the individuation of the automatic driving function and the self-adaptation of the user style, and try to shorten the adaptation period and the trust accumulation period of the user on the automatic driving function. For example, one current attempt is: an information reminding mechanism taking 'acousto-optic information' as a core is designed and delivered around an in-vehicle instrument, a central control large-screen visual interface and an in-vehicle acousto-optic system based on the requirements of danger levels, vehicle intentions and the like under different functional scenes of automatic driving.

However, in implementing the present invention, the inventors found that: although the above-mentioned simple reminding mechanism can improve the information transfer clarity and timeliness of the user in using the automatic driving function, the driving requirements of different users may be different, and the simple reminding mechanism often cannot meet the requirements of different users on the automatic driving control strategy.

Disclosure of Invention

An embodiment of the present invention provides an information processing method, an information processing apparatus, an electronic device, and a storage medium, so as to solve a problem that the prior art cannot meet requirements of different users for different automatic driving control strategies.

In one aspect, an information processing method is provided, and the method includes:

acquiring reference information of a plurality of users, wherein the reference information comprises behavior information, driving information and feedback information of the same user, the behavior information comprises behavior information of the user in terminal equipment associated with a vehicle, the driving information comprises the driving information when the vehicle logs in a user account, and the feedback information comprises use feedback information of the vehicle by the user;

pushing a software update package of an automatic driving system of the vehicle to the plurality of users according to the reference information.

In one possible embodiment, the pushing a software update package of an automatic driving system of a vehicle to the plurality of users according to the reference information includes:

acquiring a first label of the behavior information, a second label of the driving information and a third label of the feedback information aiming at the reference information of each user;

classifying the first label, the second label and the third label of the same user to obtain target identification information of the label category of each user;

pushing a software update package of an automatic driving system of a vehicle to the plurality of users according to the target identification information of the plurality of users.

In one possible embodiment, the behavior information, the driving information and the feedback information each include a plurality of pieces of data content; the first tag for acquiring the behavior information, the second tag for acquiring the driving information and the third tag for acquiring the feedback information comprise:

acquiring a first matching rule which each piece of data content in the behavior information accords with, and determining a first label corresponding to the first matching rule according to a first corresponding relationship, wherein the first corresponding relationship is a corresponding relationship between the label and the matching rule;

acquiring a second matching rule which each piece of data content in the driving information accords with, and determining a second label corresponding to the second matching rule according to the first corresponding relation;

and acquiring a third matching rule which each piece of data content in the feedback information accords with, and determining a third label corresponding to the third matching rule according to the first corresponding relation.

In a possible implementation manner, the classifying the first tag, the second tag, and the third tag of the same user to obtain target identification information of a tag category of each user includes:

and classifying the first label, the second label and the third label of the same user by adopting a semantic analysis algorithm and a machine learning algorithm to obtain target identification information of the label category of each user.

In one possible embodiment, the pushing a software update package for an automatic driving system of a vehicle to the plurality of users according to the target identification information of the plurality of users comprises:

selecting target identification information with a first parameter larger than a preset threshold value from the target identification information of the multiple users, wherein the first parameter is a ratio of the number of the same target identification information to the total number of the target identification information of the multiple users;

determining a first target driving control strategy corresponding to target identification information of which each first parameter is greater than a preset threshold value according to a second corresponding relation, wherein the second corresponding relation is the corresponding relation between the identification information of the label category and the driving control strategy;

generating a first software update package for an autonomous driving system of the vehicle based on each of the first target driving control strategies;

pushing the first software update package to the plurality of users.

In one possible embodiment, the pushing a software update package of an automatic driving system of a vehicle to the plurality of users according to the target identification information of the plurality of users comprises:

determining a second target driving control strategy corresponding to each target identification information according to a second corresponding relation, wherein the second corresponding relation is the corresponding relation between the identification information of the label category and the driving control strategy;

generating a second software update package of an automatic driving system of the vehicle according to each second target driving control strategy, wherein the second software update package is used as a second software update package of the user corresponding to the target identification information;

and pushing the second software update package to a user corresponding to the second software update package.

In one possible implementation, the behavior information, the driving information, and the feedback information respectively include a plurality of pieces of data content, and one piece of data content corresponds to one tag; the method further comprises the following steps:

carrying out artificial intelligent AI semantic understanding on data contents corresponding to the labels under the category represented by the same target identification information to obtain AI description information of the target identification information;

acquiring a search keyword;

acquiring the target identification information matched with the search keyword;

displaying the AI description information of the target identification information matching the search keyword.

In a possible implementation manner, the behavior information, the driving information, and the feedback information respectively include a plurality of pieces of data content, and one piece of data content corresponds to one tag; the method further comprises the following steps:

determining data end information of the target identification information according to a target data end of data content corresponding to a label under the category represented by the same target identification information;

acquiring a search keyword;

and displaying the data terminal information of the target identification information matched with the search keyword.

In a possible implementation manner, the determining, according to a data end of data content corresponding to a tag in a category indicated by the same target identification information, data end information of the target identification information includes:

determining information of a data end with the largest weight value in the target data ends as data end information of the target identification information according to predetermined weights of different data ends;

or,

and determining information of a data end with the largest second parameter in the target data ends as data end information of the target identification information, wherein the second parameter is a ratio of the number of the same target data end to the total number of all the target data ends.

In another aspect, there is provided an information processing apparatus, the apparatus including:

the information acquisition module is used for acquiring reference information of a plurality of users, wherein the reference information comprises behavior information, driving information and feedback information which belong to the same user, the behavior information comprises the behavior information of the user in terminal equipment associated with the vehicle, the driving information comprises the driving information when the vehicle logs in a user account, and the feedback information comprises use feedback information of the vehicle by the user;

and the pushing module is used for pushing a software update package of an automatic driving system of the vehicle to the users according to the reference information.

In one possible embodiment, the push module comprises:

the label determining sub-module is used for acquiring a first label of the behavior information, a second label of the driving information and a third label of the feedback information aiming at the reference information of each user;

the classification processing submodule is used for classifying the first label, the second label and the third label of the same user to obtain target identification information of the label category of each user;

and the pushing submodule is used for pushing a software update package of an automatic driving system of the vehicle to the users according to the target identification information of the users.

In one possible embodiment, the behavior information, the driving information and the feedback information each include a plurality of pieces of data content; the tag determination submodule is specifically configured to:

acquiring a first matching rule which each data content in the behavior information accords with, and determining a first label corresponding to the first matching rule according to a first corresponding relationship, wherein the first corresponding relationship is a corresponding relationship between the label and the matching rule;

and acquiring a third matching rule which each data content in the feedback information accords with, and determining a third label corresponding to the third matching rule according to the first corresponding relation.

In a possible implementation, the classification processing sub-module is specifically configured to:

In a possible implementation, the push submodule is specifically configured to:

pushing the first software update package to the plurality of users.

In a possible implementation, the push submodule is specifically configured to:

In a possible implementation manner, the behavior information, the driving information, and the feedback information respectively include a plurality of pieces of data content, and one piece of data content corresponds to one tag; the device further comprises:

an AI understanding module, configured to perform artificial intelligence AI semantic understanding on data content corresponding to a tag under a category represented by the same target identification information, to obtain AI description information of the target identification information;

the search word acquisition module is used for acquiring search keywords;

the matching module is used for acquiring the target identification information matched with the search keyword;

and the first display module is used for displaying the AI description information of the target identification information matched with the search keyword.

In one possible implementation, the behavior information, the driving information, and the feedback information respectively include a plurality of pieces of data content, and one piece of data content corresponds to one tag; the device further comprises:

the data end determining module is used for determining data end information of the target identification information according to a target data end of data content corresponding to a label under the category represented by the same target identification information;

the search word acquisition module is used for acquiring search keywords;

and the second display module is used for displaying the data side information of the target identification information matched with the search keyword.

In a possible implementation manner, the data side determining module is specifically configured to:

or,

and determining the information of the data end with the largest second parameter in the target data ends as the data end information of the target identification information, wherein the second parameter is the ratio of the number of the same target data end to the total number of all the target data ends.

In still another aspect, an electronic device is provided, including:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute operations performed to implement the information processing method described above.

In still another aspect, a processor-readable storage medium is provided, which stores a computer program for causing a processor to execute the information processing method described above.

One of the above technical solutions has the following advantages or beneficial effects:

in one embodiment of the invention, reference information of a plurality of users can be acquired, so that a software update package of an automatic driving system of a vehicle is pushed to the plurality of users according to the reference information, wherein the reference information comprises behavior information, driving information and feedback information of the same user, the behavior information comprises behavior information of the users in terminal equipment associated with the vehicle, the driving information comprises driving information when the vehicle logs in a user account, and the feedback information comprises use feedback information of the vehicle by the users.

Therefore, in one embodiment of the invention, a software update package of an automatic driving system of a vehicle can be pushed to a plurality of users according to behavior information of the users in the terminal equipment associated with the vehicle, driving information when the vehicle logs in a user account, and feedback information of the use of the vehicle by the users. That is, in an embodiment of the present invention, a software update package of an automatic driving system of a vehicle may be generated from the plurality of data dimensions and pushed to a user, so that a driving control strategy of the automatic driving system better meets actual requirements of the user, an automatic driving experience of the user is further improved, and an application period of the user to an automatic driving function is shortened.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate one embodiment consistent with the invention and together with the description, serve to explain the principles of the invention.

FIG. 1 is a flow diagram illustrating an information processing method according to an exemplary embodiment;

FIG. 2 is a block diagram of an information processing apparatus shown in accordance with an exemplary embodiment;

FIG. 3 is a block diagram of an electronic device shown in accordance with an example embodiment.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.

Fig. 1 is a flowchart illustrating an information processing method, which may be applied to a server, according to an exemplary embodiment, and as illustrated in fig. 1, the method may include the following steps 101 to 102:

step 101: reference information of a plurality of users is acquired.

The reference information comprises behavior information, driving information and feedback information belonging to the same user, the behavior information comprises behavior information of the user in terminal equipment associated with the vehicle, the driving information comprises the driving information when the vehicle logs in a user account, and the feedback information comprises use feedback information of the vehicle by the user.

In addition, the behavior information of the user in the terminal device associated with the vehicle may include behavior information in a plurality of Applications (APPs) (e.g., system level APP, travel APP, social APP, vehicle control APP) installed on the terminal device associated with the vehicle, such as vehicle frequency (including function operations of sending destination address through APP, remotely starting vehicle, turning on air conditioner, etc.) controlled remotely through APP, search box search record of navigation APP, trip start and end point information, trip distance/duration information, route selection preference information, call record in each trip (including call times, call duration each time, whether bluetooth device is connected or not), record of frequency of operation of the mobile device actively turned on by the user during driving, record of frequency of use of the instant messaging APP during driving of the vehicle, and use frequency of other non-vehicle own APPs.

That is, in an embodiment of the present invention, data authorization of a terminal device may be obtained, and a script is pre-embedded in APPs installed on the terminal device, so that the script is run at regular intervals to obtain the behavior information in the APPs.

The driving information may include travel information (including start and end point positioning information, travel use duration/distance), non-automatic driving style information (including acceleration and deceleration information (i.e. acceleration and brake pedal input), sharp steering information (i.e. steering wheel angle and attitude angle), average speed per hour, and starting acceleration), behavior records (including steering and pedal operation) and vehicle instant information records (including vehicle speed, heading angle, positioning and actuator task conditions) of a user actively intervening in a driving task after the driving assistance/automatic driving function is started, and current driving assistance/automatic driving task state and environment perception information records. That is, in one embodiment of the present invention, the script may be embedded in a controller on the vehicle, so that the script is executed at regular time intervals to obtain the driving information. For example, in an automatic driving mode, when a user takes off his hands and feet (namely, the hands do not touch a steering wheel, and the feet do not touch a brake and an accelerator pedal), the eyes see an adjacent vehicle target vehicle in front of one eye side, and when the current distance to the target vehicle is approximately xx m, the vehicle speed is xx km/h, and the attitude angle is xx degrees, the user actively turns the steering wheel right and emergently steps on the brake, and at the moment, the vehicle is in a call through a vehicle-mounted Bluetooth function, so that the driving information can be acquired aiming at the scene.

The feedback information may include after-sales repair contents of the vehicle and evaluation information of the vehicle function by the user. That is, in one embodiment of the present invention, the feedback information may be obtained by a vehicle operation system (e.g., an after-market vehicle system) at regular intervals.

Data buried points can be deployed through a vehicle data network at present, steering wheel turning angles, acceleration and brake pedal input and vehicle posture information of a user are continuously collected and obtained in a manual driving mode of the user, and the driving style and behaviors of the user are recorded and modeled; in the automatic driving mode, behaviors of a user actively intervening in the automatic driving function are mainly collected, for example, the user takes over steering wheel control to cause function quitting, the user actively steps on a brake to cause function quitting and the like, sensor data in a certain time before the intervention opportunity is recorded, user scene analysis is carried out, and after the user take-over reason is obtained, user targeted optimization is carried out on vehicle control under the scene. The existing scheme can perform data modeling on a driver, form driving behaviors and style copying on the learned driver, and then apply and convert the driving behaviors and style copying to a functional scene corresponding to automatic driving to provide driving experience of the driver.

Although, the current scheme can further provide automatic driving vehicle control closer to the driving behavior and style of the user on the basis of the dimensionality of the acousto-optic reminding. However, this approach collects data around the behavior/intervention of a single user on vehicle control, which is thin in the data dimension, easily leading to uncontrollable learning result bias.

In one embodiment of the invention, the reference information of a plurality of users can be acquired, the expansion of data dimension and the enrichment of data details are realized, namely, besides the data buried points need to be deployed in advance on the whole vehicle architecture level, data sources except vehicle control need to be compatible together, including the operation of software and hardware contacts in a vehicle, the operation of a vehicle machine system, user travel route information, terminal device APP data behaviors, social related behavior data and user use feedback information on the vehicle, so that automatic driving can provide a man-machine interaction strategy and related functions based on deep understanding of the real-time state of the user in a timely, suitable and self-adaptive manner.

In addition, after the behavior information, the driving information and the feedback information are acquired, the information irrelevant to the automatic driving can be deleted, so that redundant information irrelevant to the automatic driving is deleted, and a software update package of the automatic driving system can be pushed to a user more accurately according to the residual information.

Step 102: pushing a software update package of an autonomous driving system of the vehicle to the plurality of users according to the reference information.

The software update package of the automatic driving system of the vehicle is pushed to a plurality of users, and the software update package of the automatic driving system can be pushed to a vehicle-mounted terminal which logs in an account after the vehicle-mounted terminal logs in the account of one of the users; alternatively, a software update package of the automatic driving system may be pushed to an APP (for example, a vehicle control APP) of the terminal device.

In addition, after the vehicle-mounted terminal receives the installation operation of the pushed software update package, the software update package can be installed on the vehicle-mounted terminal, so that the automatic driving system on the vehicle where the vehicle-mounted terminal is located can use the driving control strategy carried by the software update package.

It should be noted here that the driving control strategy described in the embodiment of the present application includes a strategy for controlling the running of the vehicle, and a strategy for information interaction between the user and the vehicle.

After the terminal device receives a preset operation (e.g., a click operation) on the pushed software update package, the terminal device may send the software update package to the vehicle-mounted terminal to which a connection (e.g., a bluetooth connection) is established, so as to install the software update package on the vehicle-mounted terminal, and thus, an automatic driving system on a vehicle where the vehicle-mounted terminal is located may use a driving control policy carried by the software update package.

Optionally, the reference information may be collected in real time, and a software update package of the automatic driving system of the vehicle may be pushed to the plurality of users at preset time intervals according to the reference information collected at the time intervals.

Or, if the driving behavior of a user is relatively stable for a period of time, the vehicle-mounted terminal of the vehicle of the user may continuously collect the reference information of the user after installing the software update package, and if the subsequently collected reference information indicates that a deviation behavior occurs, a new software update package may be pushed to the user again according to the collected reference information after a certain deviation behavior data reaches a set threshold.

In addition, it should be noted that the plurality of users may include users of different vehicles, or may include different users of the same vehicle. And the software updating package is obtained according to the reference information, so that the driving control strategy carried by the software updating package is in accordance with the driving style and preference decision strategy of the user.

For example, user E and user F assume that they are using the same vehicle, but that both have their own cart ID accounts. And the vehicle-mounted terminal/APP terminal can actively push a software update package corresponding to the E/F of the user, so that the user can select whether to install the software update package, and the automatic driving function interaction strategy under the user ID of the vehicle login is changed into the updated interaction strategy. Therefore, when the user E uses the vehicle, the ID of the user E is automatically identified, and the driving control strategy corresponding to the adaptive style of the user E is called; and when the user F uses the vehicle, the ID of the user F is automatically identified, so that the driving control strategy corresponding to the adaptive style of the user F is called.

As can be seen from the foregoing steps 101 to 102, in an embodiment of the present invention, reference information of a plurality of users can be acquired, so that a software update package of an automatic driving system of a vehicle is pushed to the plurality of users according to the reference information, wherein the reference information includes behavior information of users in terminal devices associated with the vehicle, driving information including driving information when the vehicle logs in a user account, and feedback information including usage feedback information of the vehicle by the users.

In an information processing method according to another embodiment of the present invention, step 102 "pushing a software update package of an automatic driving system of a vehicle to the plurality of users according to the reference information" includes the following sub-steps A1 to A2:

substep A1: acquiring a first label of the behavior information, a second label of the driving information and a third label of the feedback information aiming at the reference information of each user;

substep A2: classifying the first label, the second label and the third label of the same user to obtain target identification information of the label category of each user;

substep A3: pushing a software update package of an automatic driving system of a vehicle to the plurality of users according to the target identification information of the plurality of users.

For the sub-step A1, if the information processing method according to the embodiment of the present invention is executed by a server, after the behavior information, the driving information, and the feedback information are obtained by the server, the server determines a first tag of the behavior information, a second tag of the driving information, and a third tag of the feedback information;

or, the terminal device which collects the behavior information may determine the first label of the behavior information, and then send the behavior information and the first label to the server; determining a second label of the driving information by the vehicle-mounted terminal for collecting the driving information, and then sending the driving information and the second label to a server; and determining a third label of the feedback information by a vehicle operation system (such as a vehicle after-sale system) for collecting the feedback information, and then sending the feedback information and the third label to the server.

The first tag indicates a user behavior characteristic expressed by the data content corresponding to the first tag, the second tag indicates a vehicle driving characteristic expressed by the data content corresponding to the second tag, and the third tag indicates a user feedback characteristic expressed by the data content corresponding to the third tag.

As can be seen from substep A1, it is necessary to determine the labels for the behavior information, the travel information, and the feedback information of each user.

After the first label, the second label and the third label of each user are obtained through the substep A1, the first label, the second label and the third label belonging to the same user need to be classified, so that the target identification information of the label category of each user is obtained, and then a software update package of an automatic driving system of a vehicle is pushed to each user according to the target identification information of the label category of each user.

As can be seen from the foregoing substeps A1 to A3, in an embodiment of the present invention, after the behavior information, the driving information, and the feedback information are obtained, features of each information, that is, tags, may be extracted, and then the extracted tags are classified, so that a software update package is pushed according to the type of the tags, instead of directly pushing the software update package according to the obtained behavior information, the driving information, and the feedback information, so that system overhead may be saved.

In an information processing method of another embodiment of the present invention, the behavior information, the travel information, and the feedback information respectively include a plurality of pieces of data content; the substep A1 "acquiring the first label of the behavior information, the second label of the travel information, and the third label of the feedback information" includes substeps B1 to B3 as follows:

substep B1: acquiring a first matching rule which each data content in the behavior information accords with, and determining a first label corresponding to the first matching rule according to a first corresponding relationship, wherein the first corresponding relationship is a corresponding relationship between the label and the matching rule;

substep B2: acquiring a second matching rule which each piece of data content in the driving information accords with, and determining a second label corresponding to the second matching rule according to the first corresponding relation;

substep B3: and acquiring a third matching rule which each data content in the feedback information accords with, and determining a third label corresponding to the third matching rule according to the first corresponding relation.

In a first aspect, the behavior information includes a plurality of pieces of data content, a matching rule, and a corresponding first tag, for example, as shown in table 1:

table 1 behavior information, matching rules, first tag example

As can be seen from table 1, if the data content with sequence number 1 in table 1 conforms to the rule "remote control vehicle through APP every day > first preset value", the first tag of the data content is "remote control person who arrives", and if the data content conforms to the rule "navigation times sent in advance/total number of trips > second preset value", the first tag of the data content is "planning person who arrives";

if the data content with the sequence number 2 in table 1 meets the rule that the ratio of the trip times/weekly trip times when the destination distance exceeds the first preset distance is greater than the third preset value, the first label of the data content is a long distance traveler, and if the rule that the ratio of the trip times/weekly trip times when the destination distance is less than the second preset distance is greater than the fourth preset value is met, the first label of the data content is a short distance commuter;

if the data content with the sequence number of 3 in table 1 meets the rule that the number of times of non-bluetooth calls in a single trip is less than or equal to a fifth preset value, the first label of the data content is a "weak interference call", and if the data content meets the rule that the number of times of bluetooth calls in a single trip is greater than a sixth preset value, the first label of the data content is a "strong interference call";

if the data content with the sequence number of 4 in table 1 meets the rule that the number of times of the active lighting operation of the mobile device by the user is greater than or equal to the seventh preset value in the two consecutive trips, the first label of the data content is "heavy device dependence", and if the number of times of the active lighting operation of the mobile device by the user is less than or equal to the eighth preset value in the two consecutive trips, the first label of the data content is "light device dependence";

if the data content with the sequence number of 5 in table 1 meets the rule that "total duration using instant messaging APP in a single trip > ninth preset value", the first tag of the data content is "high-risk of distraction", and if the data content meets the rule that "total duration using instant messaging APP in a single trip < tenth preset value", the first tag of the data content is "low-risk of distraction";

if the data content with the sequence number of 6 in table 1 meets the rule that "total number of times of using the own APPs of other non-vehicles in a single trip > eleventh preset value", the first tag of the data content is "high risk of distraction", and if the data content meets the rule that "total number of times of using the own APPs of other non-vehicles in a single trip < twelfth preset value", the first tag of the data content is "low risk of distraction".

In a second aspect, the travel information includes a plurality of data contents, matching rules, and corresponding second tags, as shown in table 2:

TABLE 2 example of travel information, matching rules, second tag

As can be seen from table 2, if the data content with sequence number 1 in table 2 conforms to the rule that "the ratio of trip times/weekly trip times when the destination distance exceeds the first preset distance > the third preset value", the second label of the data content is "long distance traveler", if the rule that "the ratio of trip times/weekly trip times when the destination distance is less than the second preset distance > the fourth preset value", the second label of the data content is "short distance commuter", and if the rule that "the ratio of trip times/weekly trip times when the destination distance exceeds the third preset distance exceeds the thirteenth preset value", the second label of the data content is "travel traveler";

if the data content with the sequence number 2 in table 2 conforms to the rule of "no urgent operation for a single month, and the average speed is within the predetermined speed range", the second label of the data content is "sincere style", if the data content conforms to the rule of "the number of urgent operations for a single month > the fourteenth predetermined value, and the average speed is greater than the upper limit value of the predetermined speed range", the second label of the data content is "aggressive violent style", and if the data content conforms to the rule of "the number of urgent operations for a single month < the fifteenth predetermined value, and the average speed is lower than the lower limit value of the predetermined speed range", the second label of the data content is "excessively conservative style";

if the data content with the sequence number of 3 in table 2 meets the rule of "the monthly normal trigger time is greater than the sixteenth preset value", the second label of the data content is "dangerous driving", and if the data content meets the rule that the monthly normal trigger time is less than or equal to the seventeenth preset value ", the second label of the data content is" safe driving ";

if the data content with the sequence number 4 in table 2 conforms to the rule that the behavior of the user is completely opposite to the behavior of the current assistant driving/automatic driving function, the second label of the data content is "an expected decision is contrary", if the data content conforms to the rule that the behavior of the user is the same as the behavior of the assistant driving/automatic driving function but the user applies assistance ", the second label of the data content is" an expected decision is not in line with the expectation ", and if the data content conforms to the rule that the intervention of the user does not interfere with the automatic driving function", the second label of the data content is "an expected decision is in line with the expectation".

In a third aspect, the feedback information includes a plurality of data contents, a matching rule, and a corresponding third tag, for example, as shown in table 3:

table 3 feedback information, matching rules, third tag example

As can be seen from table 3, if the data content with sequence number 1 in table 3 belongs to the "feedback of biased driving styles such as acceleration and deceleration comfort, lane change success rate/opportunity, and the like, related to the automatic driving function," the driving style is not in accordance with expectations ", if the data content belongs to the" function feedback such as safety pre-warning/braking/avoidance, and the like, related to emergency triggering, and the generated accident feedback ", the third tag of the data content is" partial scene safety/accident risk ", if the data content belongs to the" specific use and operation step doubts related to the function, "the third tag of the data content is" function incomprehensible ", and if the data content belongs to the" other new function suggestions not in the currently provided function, "the third tag of the data content is" new function experience suggestion ";

if the data content with the serial number of 2 in table 3 belongs to "risk emergence and maintenance of front and rear bumpers", the third tag of the data content is "rear-end driving accident/maintenance", if the data content belongs to "scratch existing in one circle of the vehicle body", the third tag of the data content is "medium and low speed scratch accident/maintenance", and if the data content belongs to "replacement/warranty of related hardware for automatic driving", the third tag of the data content is "repair/replacement of related hardware failure".

As described above, in an embodiment of the present invention, the first correspondence relationship between the tag and the matching rule may be predetermined, so that the matching rule to which the content of each piece of data conforms may be determined for each piece of behavior information, the piece of driving information, and the piece of feedback information, respectively, so as to determine the tag of each piece of data.

In the information processing method according to another embodiment of the present invention, sub-step A2 "classify the first tag, the second tag, and the third tag of the same user to obtain the target identification information of the tag class of each user" includes sub-step C1:

substep C1: and classifying the first label, the second label and the third label of the same user by adopting a semantic analysis algorithm and a machine learning algorithm to obtain target identification information of the label category of each user.

During the natural language processing, scenes of similar sentences or approximate expressions of sentences are often found, and the similar sentences need to be grouped together at this time, so that the natural language processing can be performed by adopting a semantic analysis algorithm. Similarly, in an embodiment of the present invention, a semantic analysis algorithm may be used to process the first tag, the second tag, and the third tag, and find out tags in similar scenes or tags expressing similar features, thereby implementing clustering processing on the tags.

In addition, machine learning is a multi-field cross discipline, and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis and algorithm complexity theory. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Therefore, in an embodiment of the present invention, the first tag, the second tag, and the third tag are classified by combining the semantic analysis algorithm and the machine learning algorithm, so that a more accurate tag category can be obtained.

For example, the first tag includes: remote control of people, long-distance travelers, strong interference conversation, severe equipment dependence and high distraction risk; the second label includes: long distance travelers, impatient style, dangerous driving; the third tag includes: the function is not understood, and the scratch accidents/maintenance are carried out at medium and low speed; the aforementioned semantic analysis algorithm and machine learning algorithm can be adopted to classify the labels according to different degrees of three dimensions of "driving experience", "functional familiarity degree", and "driving style", that is:

the two labels can indicate that a user is familiar with the simple car control function of the terminal equipment, but the familiarity of the complex automatic driving function of getting on the car is low, so the two labels can be classified under the category of moderate function familiarity;

the labels of strong interference conversation, severe equipment dependence, high distraction risk, aggressive violent style, dangerous driving and medium and low speed scratch accident/maintenance are classified under the category of dangerous driving style;

the "long distance traveler" indicates that the driving experience of the user is rich, and therefore, the tag is classified under the category of "rich driving experience".

In an information processing method according to another embodiment of the present invention, the substep A3 "pushing a software update package of an automatic driving system of a vehicle to the plurality of users according to the target identification information of the plurality of users" includes substeps D1 to D4 as follows:

substep D1: selecting target identification information with a first parameter larger than a preset threshold value from the target identification information of the multiple users, wherein the first parameter is a ratio of the number of the same target identification information to the total number of the target identification information of the multiple users;

substep D2: determining a first target driving control strategy corresponding to target identification information of which each first parameter is greater than a preset threshold value according to a second corresponding relation, wherein the second corresponding relation is the corresponding relation between the identification information of the label category and the driving control strategy;

substep D3: generating a first software update package for an automated vehicle drive system based on each of the first target drive control strategies;

substep D4: pushing the first software update package to the plurality of users.

For example, the target identification information of the plurality of users includes 10, where the number of the target identification information a is X1, the number of the target identification information B is X2, X1/10> a preset threshold, and X2/10> a preset threshold, a first target driving control policy corresponding to the target identification information a and a first target driving control policy corresponding to the target identification information B may be obtained from a second corresponding relationship between identification information of predetermined tag categories and driving control policies, so as to generate two first software update packages according to the two first target driving control policies, and then push the two first software update packages to each of the plurality of users.

It can be understood that, if there is no driving control strategy corresponding to the target identification information with the first parameter greater than the preset threshold in the second corresponding relationship, the target identification information with the first parameter greater than the preset threshold is deleted, that is, the target identification information with the first parameter greater than the preset threshold does not generate the corresponding software update package.

In addition, the first parameter of one target identification information is greater than the preset threshold value, which indicates that the number of users corresponding to the tag category indicated by the target identification information is large, and the driving control strategy corresponding to the target identification information in the second corresponding relationship is applicable to most users, so that the software update package generated according to the driving control strategy corresponding to the target identification information can be pushed to each user of the plurality of users.

In an information processing method according to another embodiment of the present invention, the substep A3 "pushing a software update package of an automatic driving system of a vehicle to the plurality of users according to the target identification information of the plurality of users" includes the substeps E1 to E3 as follows:

substep E1: determining a second target driving control strategy corresponding to each target identification information according to a second corresponding relation, wherein the second corresponding relation is the corresponding relation between the identification information of the label category and the driving control strategy;

substep E2: generating a second software update package of an automatic driving system of the vehicle according to each second target driving control strategy, wherein the second software update package is used as a second software update package of the user corresponding to the target identification information;

substep E3: and pushing the second software update package to a user corresponding to the second software update package.

For example, if the target identification information of the multiple users includes 10 pieces of target identification information, the second target driving control strategy corresponding to each piece of target identification information may be respectively obtained from the second corresponding relationship between the predetermined identification information of the tag category and the driving control strategy, so as to respectively generate a second software update package according to each second target driving control strategy, obtain 10 second software update packages, and then push the 10 second software update packages to the corresponding users (for example, the second software update package generated by the second target driving control strategy corresponding to the target identification information C is pushed to the user corresponding to the target identification information C).

It can be understood that, if there is no driving control strategy corresponding to a certain target identification information in the second corresponding relationship, the target identification information is deleted, that is, the target identification information does not generate a corresponding software update package.

As can be seen from the above, in an embodiment of the present invention, a corresponding software update package may also be generated according to the target identification information of the tag category of each user, that is, the driving control policy is personalized for each user, so that the driving control policy of the automatic driving system better meets the actual requirements of the user.

Further, the driving control strategy corresponding to the "moderate degree of functional familiarity" described above may include: the method is characterized in that a standard-style Human Machine Interface (HMI) information reminding configuration is used, a full-range using operation prompt is provided for a driving user, and the using operation prompt comprises but is not limited to modes of instrument characters, in-vehicle AI voice broadcasting and the like, and meanwhile, the vehicle control style is more conservative and more reliable so as to make up for the operation understanding time difference caused by the fact that the user is not particularly familiar with functions;

the driving control strategy corresponding to the "dangerous driving style" may include: in the automatic driving function activation, the reverse behavior of user intervention triggers an HMI (human machine interface) system of a vehicle to carry out more sensitive acousto-optic prompt, so that the user can know the risk of dangerous driving style intervention, and meanwhile, the aggressive performance under car following and lane changing scenes is provided in the control style so as to meet the expectation of the style user;

the driving control strategy corresponding to the "richer driving experience" may include: the system level information and prompt are provided only under the dangerous relevant conditions by using the mild-style HMI information reminding configuration, a non-disturbing use scene is provided for drivers and passengers, and meanwhile, the vehicle control style can also be used for learning and training according to the driving input information of the user, so that the driving behavior of the user is copied.

In an information processing method according to another embodiment of the present invention, the behavior information, the driving information, and the feedback information respectively include a plurality of pieces of data content, and one piece of data content corresponds to one tag; the method further comprises the following steps F1 to F4:

step F1: carrying out artificial intelligent AI semantic understanding on data contents corresponding to the labels under the category represented by the same target identification information to obtain AI description information of the target identification information;

step F2: acquiring a search keyword;

step F3: acquiring the target identification information matched with the search keyword;

step F4: displaying the AI description information of the target identification information matching the search keyword.

The AI description information is obtained by performing AI semantic understanding on the data content corresponding to the tag under the category indicated by the target identification information, and therefore, the AI description information is used for describing feature information of the data content corresponding to the tag under the category indicated by the target identification information, for example, if a certain target identification information is a "dangerous driving style", the AI description information is used for describing dangerous driving behaviors in the data content corresponding to the "dangerous driving style".

In addition, the AI description information of the target identification information matched with the search keyword can be searched in a way of searching the keyword, so that the searched IA description information is displayed, and the related personnel can conveniently check the IA description information.

In an information processing method according to another embodiment of the present invention, the behavior information, the driving information, and the feedback information respectively include a plurality of pieces of data content, and one piece of data content corresponds to one tag; the method further comprises the following steps G1 to G4:

step G1: determining data end information of the target identification information according to a target data end of data content corresponding to a label under the category represented by the same target identification information;

step G2: acquiring a search keyword;

step G3: acquiring the target identification information matched with the search keyword;

step G4: and displaying the data terminal information of the target identification information matched with the search keyword.

The data side information of the target identification information is used for representing the source information of the data content under the category represented by the target identification information.

In addition, the data side information of the target identification information matched with the search keyword can be searched in a keyword search mode, so that the searched data side information is displayed, and related personnel can conveniently check the data source of the target data identification information to be searched.

In the information processing method according to another embodiment of the present invention, the step G1 "determining data end information of the target identification information according to a data end of data content corresponding to a tag in a category indicated by the same target identification information" includes the following substep H1:

a substep H1 of determining information of a data end with the maximum weight value in the target data ends as data end information of the target identification information according to weights of different predetermined data ends;

or,

For example, the tag under the category represented by the target identification information Y1 includes Z1, Z2, and Z3, where Z1 is from a terminal device dimension, Z2 is from a vehicle dimension, and Z3 is from a user feedback dimension, where the weight of the predetermined vehicle dimension is the largest, and then "data end information" of the target identification information Y1 is "vehicle end";

or the labels in the category represented by the target identification information Y2 include Z4, Z5, and Z6, where Z4 is from a terminal device dimension, Z5 is from a user dimension, and Z6 is from the user dimension, and if the "user dimension" proportion is the largest, then the "data side information" of the target identification information Y2 is the "user dimension".

It can be seen that, in an embodiment of the present invention, the data side information of the target identification information may be determined according to the weight or the proportion of the data side of the data content in the category indicated by one target identification information.

In the information processing method according to another embodiment of the present invention, the above embodiments may be combined with each other.

Alternatively, an embodiment resulting in a combined manner may be described by steps H1 to H13 as follows:

step H1: acquiring reference information of a plurality of users, wherein the reference information comprises behavior information, driving information and feedback information belonging to the same user, the behavior information comprises behavior information of the users in terminal equipment associated with the vehicle, the driving information comprises the driving information when the vehicle logs in a user account, and the feedback information comprises use feedback information of the vehicle by the users;

the following steps H3 to H5 are performed for the reference information of each user;

step H3: acquiring a first matching rule which each data content in the behavior information accords with, and determining a first label corresponding to the first matching rule according to a first corresponding relation, wherein the first corresponding relation is a corresponding relation between the label and the matching rule;

step H4: acquiring a second matching rule which each data content in the driving information accords with, and determining a second label corresponding to the second matching rule according to the first corresponding relation;

step H5: acquiring a third matching rule which each data content in the feedback information accords with, and determining a third label corresponding to the third matching rule according to the first corresponding relation;

step H6: classifying the first label, the second label and the third label of the same user by adopting a semantic analysis algorithm and a machine learning algorithm to obtain target identification information of the label category of each user;

wherein, after the step H6, steps H7 to H10 may be performed, or steps H11 to H13:

step H7: selecting target identification information with a first parameter larger than a preset threshold value from the target identification information of a plurality of users, wherein the first parameter is the ratio of the number of the same target identification information to the total number of the target identification information of the plurality of users;

step H8: determining a first target driving control strategy corresponding to target identification information of which each first parameter is greater than a preset threshold value according to a second corresponding relation, wherein the second corresponding relation is the corresponding relation between the identification information of the label category and the driving control strategy;

step H9: generating a first software update package for the vehicle's autonomous driving system based on each of the first target driving control strategies;

step H10: pushing the first software update package to a plurality of users;

step H11: determining a second target driving control strategy corresponding to each target identification information according to the second corresponding relation;

step H12: generating a second software update package of the automatic driving system of the vehicle according to each second target driving control strategy, wherein the second software update package is used as a second software update package of the user corresponding to the target identification information;

step H13: and pushing the second software update package to a user corresponding to the second software update package.

In summary, compared with the conventional scheme, the embodiment of the present invention not only can implement multi-dimensional information tagging and tag clustering for the master vehicle and passenger, but also can perform learning configuration for one vehicle and multiple users/passengers. The method comprises the steps of collecting data of a user on a vehicle control layer in the prior art, further collecting data of the user in the vehicle using process, using large-screen interaction in the vehicle, voice interaction in the vehicle, APP vehicle control interaction data information, and acquiring mobile phone system/application level data of social contact and relevant social contact software of the user in a rim on the premise of user authorization, so that the familiarity, driving experience and driving style of the user to all relevant functions of the vehicle in the vehicle using process are comprehensively analyzed and known from multi-dimensional information, and therefore automatic driving can be timely, suitable and self-adaptive, and a man-machine interaction strategy and relevant functions based on deep understanding of the real-time state of the user are provided.

Fig. 2 is a block diagram illustrating an information processing apparatus according to an exemplary embodiment, which may include the following modules:

the information acquisition module 201 is configured to acquire reference information of multiple users, where the reference information includes behavior information, driving information, and feedback information of the same user, the behavior information includes behavior information of a user in a terminal device associated with a vehicle, the driving information includes driving information when the vehicle logs in a user account, and the feedback information includes feedback information of use of the vehicle by the user;

a pushing module 202, configured to push a software update package of an automatic driving system of a vehicle to the plurality of users according to the reference information.

In one possible implementation, the pushing module 202 includes:

In a possible implementation, the push submodule is specifically configured to:

pushing the first software update package to the plurality of users.

In a possible implementation, the push submodule is specifically configured to:

the search word acquisition module is used for acquiring search keywords;

and the second display module is used for displaying the data terminal information of the target identification information matched with the search keyword.

determining the information of the data end with the largest weight value in the target data ends as the data end information of the target identification information according to the predetermined weights of different data ends;

or,

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, where the program may be stored in a computer readable storage medium, and when executed, the program includes the following steps:

for each of the reference information of the users,

acquiring a first label of the behavior information, a second label of the driving information and a third label of the feedback information;

generating a first software update package for an automated vehicle drive system based on each of the first target drive control strategies;

pushing the first software update package to the plurality of users.

acquiring a search keyword;

In a possible implementation manner, the determining, according to a data end of data content corresponding to a tag in a category represented by the same target identification information, data end information of the target identification information includes:

or,

The storage medium is, for example: read-Only Memory (ROM)/Random Access Memory (RAM), magnetic disk, optical disk, and the like.

The embodiment of the invention also provides the electronic equipment which can be a vehicle-mounted terminal.

As shown in fig. 3, the electronic device includes a memory 320, a transceiver 310, a processor 300;

a memory 320 for storing a computer program;

a transceiver 310 for receiving and transmitting data under the control of the processor 300;

a processor 300 for reading the computer program in the memory 320 and executing the information processing method described above.

Where, in fig. 3, the bus architecture may include any number of interconnected buses and bridges, one or more of the processor 300, represented by the processor 300, and various circuits of the memory 320, represented by the memory 320, are coupled together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The bus interface is used to provide an interface. The transceiver 310 may be a number of elements including a transmitter and a receiver that provide a means for communicating with various other apparatus over transmission media including wireless channels, wired channels, fiber optic cables, and the like. For different user devices, the user interface 330 may also be an interface capable of interfacing with a desired device externally, including but not limited to a keypad, display, speaker, microphone, joystick, etc.

The processor 300 is responsible for managing the bus architecture and general processing, and the memory 320 may store data used by the processor 300 in performing operations.

Alternatively, the processor 300 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or a Complex Programmable Logic Device (CPLD), and the processor 300 may also adopt a multi-core architecture.

The processor 300 is configured to execute any of the methods provided by the embodiments of the present invention by calling the computer program stored in the memory 320 according to the obtained executable instructions. The processor 300 and the memory 320 may also be physically separated.

It should be noted that, the electronic device provided in the embodiment of the present invention can implement all the method steps implemented by the method embodiment and achieve the same technical effect, and detailed descriptions of the same parts and beneficial effects as the method embodiment in this embodiment are omitted here.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims

1. An information processing method, characterized in that the method comprises:

pushing a software update package of an autonomous driving system of the vehicle to the plurality of users according to the reference information.

2. The method of claim 1, wherein pushing a software update package for an autonomous driving system of a vehicle to the plurality of users according to the reference information comprises:

aiming at the reference information of each user, acquiring a first label of the behavior information, a second label of the driving information and a third label of the feedback information;

3. The method according to claim 2, wherein the behavior information, the travel information, and the feedback information respectively include a plurality of pieces of data content;

the first tag for acquiring the behavior information, the second tag for acquiring the driving information and the third tag for acquiring the feedback information comprise:

4. The method according to claim 2, wherein the classifying the first tag, the second tag, and the third tag of the same user to obtain the target identification information of the tag category of each user includes:

5. The method of claim 2, wherein pushing a software update package for an autonomous driving system of a vehicle to the plurality of users according to the target identification information of the plurality of users comprises:

pushing the first software update package to the plurality of users.

6. The method of claim 2, wherein pushing a software update package for an autonomous driving system of a vehicle to the plurality of users according to the target identification information of the plurality of users comprises:

7. The method according to claim 2, wherein the behavior information, the driving information and the feedback information respectively comprise a plurality of data contents, and one tag is corresponding to one data content; the method further comprises the following steps:

acquiring a search keyword;

8. The method according to claim 2, wherein the behavior information, the driving information, and the feedback information respectively include a plurality of pieces of data content, and one piece of data content corresponds to one tag;

the method further comprises the following steps:

acquiring a search keyword;

9. The method according to claim 8, wherein the determining the data side information of the target identification information according to the data side of the data content corresponding to the tag in the category indicated by the same target identification information includes:

or,

10. An information processing apparatus characterized in that the apparatus comprises:

11. The apparatus of claim 10, wherein the push module comprises:

the label determination submodule is used for acquiring a first label of the behavior information, a second label of the driving information and a third label of the feedback information aiming at the reference information of each user;

12. The apparatus according to claim 11, wherein the behavior information, the travel information, and the feedback information respectively include a plurality of pieces of data content;

the tag determination submodule is specifically configured to:

13. The apparatus according to claim 11, wherein the classification processing sub-module is specifically configured to:

14. The apparatus of claim 11, wherein the push submodule is specifically configured to:

pushing the first software update package to the plurality of users.

15. The apparatus of claim 11, wherein the push submodule is specifically configured to:

16. The apparatus according to claim 11, wherein the behavior information, the driving information, and the feedback information respectively include a plurality of pieces of data content, and one piece of data content corresponds to one tag; the device further comprises:

the search word acquisition module is used for acquiring search keywords;

17. The apparatus according to claim 11, wherein the behavior information, the driving information, and the feedback information respectively include a plurality of pieces of data content, and one piece of data content corresponds to one tag; the device further comprises:

the search word acquisition module is used for acquiring search keywords;

18. The apparatus of claim 17, wherein the data side determining module is specifically configured to:

or,

19. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute to implement the operations performed by the information processing method of any one of claims 1 to 9.

20. A processor-readable storage medium, characterized in that the processor-readable storage medium stores a computer program for causing the processor to execute the information processing method of any one of claims 1 to 9.