Vehicle cockpit information processing method and related device and storage medium and program

Abstract

The embodiment of the present application provides an information processing method based on shoulder belt status detection for a vehicle cabin, and related devices, storage media, and programs. The information processing method includes: obtaining preset status data of the vehicle cabin, the preset status data including at least one of the following: multiple tension data of the shoulder belt restraint module collected and fed back by the smart safety seat, and multiple images of children on the smart safety seat collected and fed back by the front in-vehicle camera of the vehicle cabin; detecting the use status of the shoulder belt of the shoulder belt restraint module according to the preset status data, the use status including abnormal use status and normal use status; if the use status of the shoulder belt of the shoulder belt restraint module is detected to be an abnormal use status, the automatic reminder mechanism of the smart cabin domain controller is triggered; if the use status of the shoulder belt of the shoulder belt restraint module is detected to be a normal use status, the current detection cycle processing is marked as finished, and the operation of the next detection cycle is continued after waiting for a preset time.

Description

Technical Field

[0001] The present application belongs to the general data processing technology field of the Internet industry, and specifically relates to an information processing method for a vehicle cockpit based on shoulder strap status detection, and related devices, storage media, and programs. Background Art

[0002] The shoulder strap fixation on the smart child safety seat is very important. It can effectively fix the child to prevent the child from shaking up and down or shaking left and right when the vehicle is bumpy, and prevent the child from falling off or falling off the smart child safety seat in an extremely bumpy state. Since the smart child safety seat needs to use shoulder straps, waist protectors, etc. to fix the child in the smart child safety seat, in actual situations, the child may feel uncomfortable or active, or the shoulder straps may not be completely fixed when the child is fixed in the smart child safety seat, causing the shoulder straps to fall off or be ineffectively fixed. At this time, there will be safety hazards, and a reminder and handling mechanism for abnormal use of the shoulder straps is urgently needed. Summary of the invention

[0003] The embodiment of the present application provides a vehicle cabin information processing method and device based on shoulder strap status detection. In the present application, by detecting the shoulder straps of the smart safety seat to determine whether the shoulder straps are in an abnormal use state, and in the case that the shoulder straps of the smart safety seat are in an abnormal use state, parents can be reminded in time to make corrections to improve safety.

[0004] In a first aspect, an embodiment of the present application provides an information processing method for a vehicle cockpit based on shoulder belt restraint module status detection. The information processing method is applied to a smart cockpit domain controller of a vehicle cockpit, and the smart cockpit domain controller is communicatively connected with a smart safety seat; the information processing method comprises:

[0005] Acquiring preset state data of the vehicle cabin, the preset state data comprising at least one of the following: a plurality of tension data of a shoulder belt restraint module collected and fed back by the smart safety seat, and a plurality of images of a child on the smart safety seat collected and fed back by a front-row in-vehicle camera of the vehicle cabin;

[0006] Detecting the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data, wherein the use state includes an abnormal use state and a normal use state, wherein the abnormal use state is used to indicate that the shoulder strap is in a detached or unfastened state, and the normal use state is used to indicate that the shoulder strap is in a fastened and stably restrained state;

[0007] If it is detected that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state, the automatic reminder mechanism of the smart cockpit domain controller is triggered, and the automatic reminder mechanism is used to remind the occupants other than the children on the smart safety seat in the vehicle cabin that the shoulder strap of the shoulder strap restraint module is in the abnormal use state through voice reminder and / or information display, and remind the occupants to correct the shoulder strap of the shoulder strap restraint module;

[0008] If it is detected that the use state of the shoulder strap of the shoulder strap restraint module is a normal use state, the current detection cycle processing is marked as finished, and the operation of the next detection cycle is continued after waiting for a preset time.

[0009] In a second aspect, an embodiment of the present application provides an information processing system for a vehicle cockpit based on shoulder belt restraint module status detection. The information processing system is applied to an intelligent cockpit domain controller of the vehicle cockpit, the intelligent cockpit domain controller is communicatively connected with an intelligent safety seat, and the information processing system includes:

[0010] an acquisition module, configured to acquire preset state data of the vehicle cabin, wherein the preset state data includes at least one of the following: a plurality of tension data of a shoulder belt restraint module collected and fed back by the smart safety seat, and a plurality of images of a child on the smart safety seat collected and fed back by a front-row in-vehicle camera of the vehicle cabin;

[0011] a detection module, configured to detect the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data, wherein the use state includes an abnormal use state and a normal use state, wherein the abnormal use state is used to indicate that the shoulder strap is in a detached or unfastened state, and the normal use state is used to indicate that the shoulder strap is in a fastened and stably restrained state;

[0012] a trigger module, configured to trigger an automatic reminder mechanism of the smart cockpit domain controller if it is detected that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state, wherein the automatic reminder mechanism is configured to remind the occupants other than the children on the smart safety seat in the vehicle cockpit that the shoulder strap of the shoulder strap restraint module is in the abnormal use state through voice reminder and / or information display, and to remind the occupants to correct the shoulder strap of the shoulder strap restraint module;

[0013] The marking module is used to mark the end of the current detection cycle processing if it is detected that the use state of the shoulder strap of the shoulder strap restraint module is the normal use state, and wait for a preset time to continue the operation of the next detection cycle.

[0014] In a third aspect, an embodiment of the present application provides an electronic device. The electronic device includes a processor and a memory, the processor and the memory are connected to each other, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the information processing method as described in the first aspect.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the information processing method as described in the first aspect.

[0016] In the information processing method and device based on shoulder belt status detection of the vehicle cockpit of the present application, the information processing method is applied to the smart cockpit domain controller of the smart cockpit, the smart cockpit domain controller is communicatively connected with the smart safety seat, and the preset status data of the smart cockpit is obtained through the smart cockpit domain controller, and the preset status data is at least one of the following: multiple tension data of the shoulder belt restraint module collected and fed back by the smart safety seat, and multiple images of the child on the smart safety seat collected and fed back by the front in-vehicle camera in the vehicle cockpit; the usage status of the shoulder belt of the shoulder belt restraint module is judged by at least one of the preset status data, and when the usage status is an abnormal usage status, the automatic reminder mechanism of the smart cockpit domain controller is triggered to promptly notify the occupants other than the children on the smart safety seat in the vehicle cockpit that the current usage status of the shoulder belt of the shoulder belt restraint module is an abnormal usage status, and remind the occupants to promptly correct the shoulder belt of the shoulder belt restraint module to improve the safety of the use of the smart safety seat. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The following is an introduction to the drawings used in the embodiments of the present application.

[0018] Figure 1 It is a structural diagram of a vehicle intelligent cockpit domain control provided by an embodiment of the present application;

[0019] Figure 2 It is a flow chart of an information processing method for a vehicle cabin based on shoulder belt restraint module status detection provided by an embodiment of the present application;

[0020] Figure 3 is a schematic diagram of an intelligent safety seat provided in an embodiment of the present application;

[0021] Figure 4 is a schematic diagram of another intelligent safety seat provided in an embodiment of the present application;

[0022] Figure 5It is a schematic diagram of the architecture of an information processing system for a vehicle cabin based on shoulder belt restraint module status detection provided by an embodiment of the present application;

[0023] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0025] The term "and / or" in this application is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship.

[0026] The "multiple" that appears in the embodiments of the present application refers to two or more. The first, second, etc. descriptions that appear in the embodiments of the present application are only used for illustration and distinction of the description objects. There is no order, nor does it represent a special limitation on the number of devices in the embodiments of the present application, and cannot constitute any limitation on the embodiments of the present application. The "connection" that appears in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not make any limitations on this.

[0027] See also Figure 1 , Figure 1 A schematic diagram of the architecture of a vehicle's intelligent cockpit domain controller is provided. Figure 1 As shown, the traditional functions of a vehicle (such as a car) include: power domain, chassis domain and body domain, and the emerging functions of a car may include: smart cockpit domain and autonomous driving domain, etc. Different controllers (such as MCU) may be set for different domains. It is understandable that multiple domains may share one controller. This application does not limit the number of controllers corresponding to each domain, and multiple controllers may be configured for one domain. The above controller may also be a general-purpose processor, image processor, or other processor with stronger computing power.

[0028] It can be understood that the smart cockpit domain controller can achieve communication connection with various components in the vehicle cockpit, so that the smart cockpit domain controller can obtain data or signals collected and reported by various components in the vehicle cockpit.

[0029] The shoulder strap fixation on the smart child safety seat is very important. It can effectively fix the child to prevent the child from shaking up and down or shaking left and right when the vehicle is bumpy, and prevent the child from falling off or falling off the smart child safety seat in an extremely bumpy state. Since the smart child safety seat needs to use shoulder straps, waist protectors, etc. to fix the child in the smart child safety seat, in actual situations, the child may feel uncomfortable or active, or the shoulder straps may not be completely fixed when the child is fixed in the smart child safety seat, causing the shoulder straps to fall off or be ineffectively fixed. At this time, there will be safety hazards, and a reminder and handling mechanism for abnormal use of the shoulder straps is urgently needed.

[0030] Please combine Figure 3 In order to solve the above problems, an embodiment of the present application provides an information processing method based on shoulder belt status detection in a vehicle cockpit domain. The information processing method is applied to a smart cockpit domain controller of a smart cockpit, and the smart cockpit domain controller is communicatively connected with a smart safety seat 300. The vehicle cockpit of the present application is based on an information processing method for shoulder belt status detection. The information processing method is applied to a smart cockpit domain controller of the smart cockpit. The smart cockpit domain controller is connected to the smart safety seat 300 in communication. The preset status data of the smart cockpit is obtained through the smart cockpit domain controller. The preset status data is at least one of the following: multiple tension data of the shoulder belt restraint module collected and fed back by the smart safety seat 300, and multiple images of the child on the smart safety seat 300 collected and fed back by the front in-vehicle camera in the vehicle cockpit; the use status of the shoulder belt of the shoulder belt restraint module is judged by at least one of the preset status data, and when the use status is an abnormal use status, the automatic reminder mechanism of the smart cockpit domain controller is triggered, so as to promptly notify the occupants other than the children on the smart safety seat 300 in the vehicle cockpit that the current use status of the shoulder belt of the shoulder belt restraint module is an abnormal use status, and remind the occupants to promptly correct the shoulder belt of the shoulder belt restraint module to improve the safety of the use of the smart safety seat 300.

[0031] There are various types of sensors installed in the vehicle cabin, such as image sensors (such as image sensors installed in the front in-vehicle camera), distance sensors, etc. Among them, the installation position of the image sensor can be any position, for example, the vehicle's interior rearview mirror, the rear or front side of the door, or the rear side of the front seat, etc., and this application does not limit the installation position of the image sensor. The distance sensor can be installed on the door of the vehicle (such as the rear door). The power type of the vehicle of this application is not limited. The vehicle can be a pure electric vehicle, a hybrid vehicle, a fuel vehicle, a methanol vehicle, or a hydrogen energy vehicle. This application does not limit the power of the vehicle.

[0032] The above information processing method is applied to the field of vehicles (such as automobiles), and the smart cockpit has a smart cockpit domain controller, which is connected to the smart safety seat 300 in communication, and the smart cockpit domain controller can obtain preset state data of the vehicle cockpit, and the preset state data includes: multiple tension data of the shoulder belt restraint module collected and fed back by the smart safety seat 300, and collected by the front in-vehicle camera of the vehicle cockpit. The smart safety seat 300 has a smart safety interface, which can be an ISOFIX interface or a LATCH interface, and the smart cockpit domain controller can detect whether the safety interface of the smart safety seat 300 is connected.

[0033] See also Figure 2 , Figure 2 This is a flowchart of an information processing method for a vehicle cabin based on shoulder strap restraint module status detection provided by an embodiment of the application. Figure 3 and Figure 4 The information processing method includes the following steps S201-S203, wherein:

[0034] S201: Acquire preset state data of the vehicle cabin, the preset state data including at least one of the following: multiple tension data of the shoulder belt restraint module collected and fed back by the smart safety seat 300, and multiple images of the child on the smart safety seat 300 collected and fed back by the front in-vehicle camera of the vehicle cabin.

[0035] The smart cockpit domain controller can obtain multiple tension data through the tension sensor installed on the smart safety seat 300, and obtain multiple images of the child on the smart safety seat 300 through the front in-vehicle camera. Among them, if the multiple images do not contain the image data of the shoulder straps of the shoulder strap restraint module, the smart cockpit domain controller can control the front in-vehicle camera to adjust the shooting angle so that the multiple images all contain the image data of the shoulder straps of the shoulder strap restraint module, and then process at least one of the obtained preset status data to determine whether the shoulder straps of the shoulder strap restraint module are in an abnormal usage state.

[0036] S202: Detecting the usage status of the shoulder straps of the shoulder strap restraint module according to preset status data, the usage status including an abnormal usage status and a normal usage status, the abnormal usage status is used to indicate that the shoulder straps are in a detached or unfastened state, and the normal usage status is used to indicate that the shoulder straps are in a fastened and stably restrained state.

[0037] In the first embodiment, the shoulder straps of the shoulder strap restraint module include a left shoulder strap 301 and a right shoulder strap 303, and the left independent retractable component of the shoulder strap restraint module is connected to the left shoulder strap 301, and the right independent retractable component of the shoulder strap restraint module is connected to the right shoulder strap 303; the preset state data includes multiple images and multiple tension data; the use state of the shoulder straps of the shoulder strap restraint module is detected according to the preset state data, including: shoulder strap recognition is performed on multiple images of the child on the smart safety seat 300 collected and fed back by the front in-vehicle camera in the vehicle cabin, and a recognition result is obtained; if the recognition result is that the multiple images only contain image data of a single shoulder strap, a first detection result of the single shoulder strap is determined according to the image data of the single shoulder strap; if the first detection result indicates If the shoulder strap on one side is in normal use state, continue to detect the shoulder strap on the other side to obtain a second detection result; if the second detection result indicates that the shoulder strap on the other side is in normal use state, then the use state of the shoulder strap of the shoulder strap restraint module is determined to be normal use state; if the second detection result indicates that the shoulder strap on the other side is in abnormal use state, then the use state of the shoulder strap of the shoulder strap restraint module is determined to be abnormal use state; if the first detection result indicates that the shoulder strap on one side is in abnormal use state, then the use state of the shoulder strap of the shoulder strap restraint module is determined to be abnormal use state; if the recognition result is that multiple images simultaneously contain image data of the left shoulder strap 301 and the right shoulder strap 303, then the use state of the shoulder strap of the shoulder strap restraint module is determined according to the image data of the left shoulder strap 301 and the right shoulder strap 303.

[0038] After the front in-car camera captures multiple images of the child in the smart safety seat 300, the smart cockpit domain controller performs shoulder strap recognition on the multiple images. When the multiple images contain image data of the shoulder straps, the status of the shoulder straps is detected; if none of the multiple images contain image data of the shoulder straps, the smart cockpit domain controller controls the front in-car camera to adjust the angle and then capture multiple images of the child in the smart safety seat 300.

[0039] For example, when the smart safety seat 300 is installed on the left rear seat or the right rear seat of the vehicle cabin, the multiple images of the child in the smart safety seat 300 captured by the front in-vehicle camera of the vehicle cabin include image information of a single shoulder strap. Figure 3 As shown, Figure 3 3 is a schematic diagram of a smart safety seat 300 provided in an embodiment of the present application. The smart safety seat 300 is installed on the rear right seat of the vehicle cabin. Due to the obstruction of the front seat directly in front of the smart safety seat 300, the multiple images collected by the front in-vehicle camera only include the image data of the right shoulder strap 303. The smart cabin domain controller determines the first detection result of the right shoulder strap 303 according to the image data of the right shoulder strap 303.

[0040] In a possible implementation, when the recognition result is that multiple images only contain image data of the right shoulder strap 303, the implementation method of determining the first detection result of the right shoulder strap 303 based on the image data of the right shoulder strap 303 may be: obtaining multiple first matching degrees between the image data of the right shoulder strap 303 and a preset normal state image of the right shoulder strap; if the multiple first matching degrees are all within the preset matching degree range, determining that the first detection result is a normal usage state; if the multiple first matching degrees include a matching degree that is not within the preset matching degree range, determining that the first detection result is an abnormal usage state.

[0041] When the first detection result of the right shoulder strap 303 indicates that the right shoulder strap 303 is in an abnormal use state (i.e., in a state of falling off or not fastened), it is determined that the shoulder strap of the shoulder strap restraint module is in an abnormal use state. When the first detection result of the right shoulder strap 303 indicates that the right shoulder strap 303 is in a normal use state (i.e., in a state of fastening and stable restraint), the intelligent cockpit domain controller continues to detect the left shoulder strap 301 to obtain a second detection result, and determines the use state of the shoulder strap of the shoulder strap restraint module according to the second detection result.

[0042] In a possible implementation, when the recognition result is that multiple images only contain image data of the right shoulder strap 303, and the first detection result indicates that the right shoulder strap 303 is in normal use, the implementation method of continuing to detect the left shoulder strap 301 to obtain the second detection result of the left shoulder strap 301 may be: sending a data request message to the smart safety seat 300, the data request message is used to request multiple tension data of the left independent retracting component to which the left shoulder strap 301 belongs; receiving a data response message from the smart safety seat 300; parsing the data response message to obtain multiple tension data of the left independent retracting component to which the left shoulder strap 301 belongs; if the analysis of the multiple tension data determines that the left shoulder strap 301 is in a detached or unfastened state, then the second detection result is determined to be an abnormal use state; if the analysis of the multiple tension data determines that the left shoulder strap 301 is in a fastened and stably constrained state, then the second detection result is determined to be a normal use state.

[0043] It can be understood that the left-side independent retraction assembly to which the left shoulder strap 301 belongs is installed with a tension sensor. Similarly, the right-side independent retraction assembly to which the right shoulder strap 303 belongs is also installed with a tension sensor, so that the intelligent cockpit domain controller can respectively obtain multiple tension data of the left-side independent retraction assembly to which the left shoulder strap 301 belongs and multiple tension data of the right-side independent retraction assembly to which the right shoulder strap 303 belongs.

[0044] In this embodiment, the smart cockpit domain controller detects that the right shoulder strap 303 is in a fastened and stably constrained state based on the first detection result of the right shoulder strap 303, and further determines whether the left shoulder strap 301 is in a fastened and stably constrained state based on multiple tension data of the left independent contraction component to which the left shoulder strap 301 belongs. Specifically, the smart cockpit domain controller analyzes whether the multiple tension data of the left independent contraction component to which the left shoulder strap 301 belongs are all within the preset force range. When the multiple tension data of the left independent contraction component to which the left shoulder strap 301 belongs are all within the preset force range, the second detection result is that the left shoulder strap 301 is in a normal use state. Since the first detection result indicates that the right shoulder strap 303 is in a normal use state, the smart cockpit domain controller determines that the shoulder strap of the shoulder strap restraint module is in a normal use state. When the multiple tension data of the left independent retraction assembly to which the left shoulder strap 301 belongs contain tension data that is not within the preset force range, the second detection result is that the left shoulder strap 301 is in an abnormal usage state, and the intelligent cockpit domain controller determines that the shoulder strap of the shoulder strap restraint module is in a normal usage state.

[0045] It can be understood that when the smart safety seat 300 is installed on the left rear seat, due to the obstruction of the front seat directly in front of the smart safety seat 300, the multiple images collected by the front in-vehicle camera only include the image data of the left shoulder strap 301. At this time, the first detection result of the left shoulder strap 301 is determined according to the image data of the left shoulder strap 301. When the first detection result indicates that the left shoulder strap 301 is in a normal use state, the right shoulder strap 303 is continuously detected to obtain the second detection result of the right shoulder strap 303. Among them, the implementation method of determining the first detection result of the left shoulder strap 301 according to the image data of the left shoulder strap 301 is similar to the implementation method of determining the first detection result of the right shoulder strap 303 according to the image data of the right shoulder strap 303, and the implementation method of detecting the right shoulder strap 303 to obtain the second detection result of the right shoulder strap 303 is similar to the implementation method of detecting the left shoulder strap 301 to obtain the second detection result of the left shoulder strap 301, which will not be repeated here.

[0046] For example, when the smart safety seat 300 is installed on the middle seat in the back row, the multiple images collected by the front in-vehicle camera include the image data of the left shoulder belt 301 and the right shoulder belt 303. At this time, the smart cockpit domain controller determines the use status of the shoulder belt of the shoulder belt restraint module based on the image data of the left shoulder belt 301 and the right shoulder belt 303.

[0047] In a possible embodiment, the method for determining the usage status of the shoulder straps of the shoulder strap restraint module based on the image data of the left shoulder strap 301 and the right shoulder strap 303 may be: obtaining multiple first matching degrees between the image data of the right shoulder strap 303 and a preset normal state image of the right shoulder strap; obtaining multiple second matching degrees between the image data of the left shoulder strap 301 and a preset normal state image of the left shoulder strap; if the multiple first matching degrees and the multiple second matching degrees are all within the preset matching degree range, then determining that the usage status of the shoulder straps of the shoulder strap restraint module is a normal usage state; if the multiple first matching degrees and the multiple second matching degrees include one or more that are not within the preset matching degree range, then determining that the usage status of the shoulder straps of the shoulder strap restraint module is an abnormal usage state.

[0048] It should be noted that, in this embodiment, the multiple images and the multiple tension data are all acquired at multiple time nodes within the current detection cycle. For example, the multiple images are acquired at time nodes t0, t1, t2, and t3, and the multiple tension data can be acquired at four time nodes after t3 and after the smart cockpit domain controller obtains the first detection result.

[0049] It can be seen that in this embodiment, for the shoulder strap restraint module in which the left independent retractable component is connected to the left shoulder strap 301 and the right independent retractable component is connected to the right shoulder strap 303, the usage status of the shoulder straps of the shoulder strap restraint module is comprehensively analyzed in combination with multiple images and multiple tension data. When the smart safety seat 300 is installed in different positions in the vehicle cabin, it is ensured that the smart cabin domain controller can accurately determine whether the left shoulder strap 301 and the right shoulder strap 303 are both in a fastened and stably restrained state, thereby improving the accuracy of triggering the automatic reminder mechanism.

[0050] See also Figure 4 , Figure 4It is a schematic diagram of another smart safety seat 300 provided in an embodiment of the present application. In the second embodiment, the shoulder straps of the shoulder strap restraint module include a left shoulder strap 301 and a right shoulder strap 303, and the shoulder strap restraint module is connected to the combined belt 305 with a single integrated retractable component, and the combined belt 305 is a restraint belt that connects the left shoulder strap 301 and the right shoulder strap 303; the preset state data includes multiple images and multiple tension data; the use state of the shoulder straps of the shoulder strap restraint module is detected according to the preset state data, including: shoulder strap recognition is performed on multiple images of the child on the smart safety seat 300 collected and fed back by the front in-vehicle camera in the vehicle cabin to obtain a recognition result; if the recognition result is that the multiple images contain image data of at least one side of the shoulder strap, a first detection result is determined according to the image data; if the first detection result is detected to indicate If it is detected that the shoulder strap on at least one side is in an abnormal usage state, then the usage state of the shoulder strap of the shoulder strap restraint module is determined to be an abnormal usage state; if it is detected that the first detection result indicates that the shoulder strap on at least one side is in a normal usage state, then the usage state of the shoulder strap of the shoulder strap restraint module is determined to be a normal usage state; if the recognition result is that the multiple images do not contain the left shoulder strap 301 or the right shoulder strap 303, then the merged belt 305 is continued to be detected to obtain a second detection result; if the second detection result indicates that the merged belt 305 is in an abnormal usage state, then the usage state of the shoulder strap of the shoulder strap restraint module is determined to be an abnormal usage state; if the second detection result indicates that the merged belt 305 is in a normal usage state, then the usage state of the shoulder strap of the shoulder strap restraint module is determined to be a normal usage state.

[0051] In a possible implementation, a method for determining the first detection result based on image data may be: obtaining multiple matching degrees between the image data and a preset normal state image of the shoulder strap; if the multiple matching degrees are all within a preset matching degree range, determining that the first detection result is a normal usage state; if the multiple matching degrees include a matching degree that is not within the preset matching degree range, determining that the first detection result is an abnormal usage state.

[0052] Since the combined belt 305 is a restraining belt connecting the left shoulder belt 301 and the right shoulder belt 303, when one shoulder belt is in a fastened and stably restrained state, it indicates that the other shoulder belt is also in a fastened and stably restrained state.

[0053] It should be noted that the preset shoulder strap normal state image includes the preset left shoulder strap 301 normal state and right shoulder strap normal state images, the preset left shoulder strap normal state image and the preset right shoulder strap normal state image. When the recognition result is that multiple images contain the image data of the left shoulder strap 301, the smart cockpit domain controller obtains the preset left shoulder strap normal state image; when the recognition result is that multiple images contain the image data of the right shoulder strap 303, the smart cockpit domain controller obtains the preset right shoulder strap normal state image; when the recognition result is that multiple images contain the image data of the left shoulder strap 301 and the right shoulder strap 303, the smart cockpit domain controller obtains the preset left shoulder strap 301 normal state and the right shoulder strap normal state images.

[0054] For example, when the plurality of images do not include the image data of the left shoulder strap 301 or the right shoulder strap 303 , the merged strap 305 is continuously detected to obtain a second detection result.

[0055] In a possible implementation, the implementation method of continuing to detect the merging belt 305 to obtain a second detection result may be: sending a data request message to the smart safety seat 300, the data request message being used to request multiple tension data of the integrated retraction component to which the merging belt 305 belongs; receiving a data response message from the smart safety seat 300; parsing the data response message to obtain multiple tension data of the integrated retraction component to which the merging belt 305 belongs; if analyzing the multiple tension data determines that the merging belt 305 is in a detached or unfastened state, then determining that the use state of the shoulder straps of the shoulder strap restraint module is an abnormal use state; if analyzing the multiple tension data determines that the merging belt 305 is in a fastened and stably constrained state, then determining that the use state of the shoulder straps of the shoulder strap restraint module is a normal use state.

[0056] When the image data containing the shoulder strap is not collected, it is determined whether the merging belt 305 is in a fastened and stably restrained state according to the multiple tension data of the integrated retracting assembly to which the merging belt 305 belongs. For example, the smart cockpit domain controller determines whether the merging belt 305 is in a fastened and stably restrained state according to the multiple tension data of the integrated retracting assembly to which the merging belt 305 belongs and the preset force range.

[0057] S203: If it is detected that the shoulder straps of the shoulder strap restraint module are in an abnormal usage state, the automatic reminder mechanism of the smart cockpit domain controller is triggered. The automatic reminder mechanism is used to remind the passengers other than children on the smart safety seat 300 in the vehicle cabin that the shoulder straps of the shoulder strap restraint module are in an abnormal usage state through voice reminder and / or information display reminder, and to remind the passengers to correct the shoulder straps of the shoulder strap restraint module.

[0058] In a possible implementation, before triggering the automatic reminder mechanism of the smart cockpit domain controller, it may also include: collecting video data of the child in the smart safety seat 300 through the front in-vehicle camera in the vehicle cockpit; and determining whether the child is in a sleeping state based on the video data.

[0059] For example, the smart cockpit domain controller can control the front in-vehicle camera to collect video data of the child in the smart safety seat 300 to determine whether the child in the video data is in a sleeping state. For example, the smart cockpit domain controller determines whether the child is in a sleeping state based on whether each frame of the video data changes. If multiple frames of the video data change, it is determined that the child is in a non-sleeping state. If multiple frames of the video data do not change, it is determined that the child is in a sleeping state.

[0060] In a possible implementation, the automatic reminder mechanism includes a voice reminder mode and an information display reminder mode, and the method for triggering the automatic reminder mechanism of the smart cockpit domain controller may be: when the video data indicates that the child is in a non-sleeping state, the voice reminder mode and / or the information display reminder mode of the smart cockpit domain controller is triggered. When the video data indicates that the real-time state of the child is in a sleeping state, the information display reminder mode of the smart cockpit domain controller is triggered.

[0061] When a child is sleeping, the smart cockpit domain controller can display information reminders through the vehicle's center console to avoid waking up the child with voice reminders. After seeing the prompt message displayed on the vehicle's center console, the occupants can adjust the shoulder straps in time to prevent accidents and improve safety.

[0062] When the child is not sleeping, the smart cockpit domain controller triggers a voice reminder to ensure that the occupants can promptly understand that the current shoulder belt is in an abnormal usage state. For example, the occupants include the driver and other passengers sitting in the back seat. After hearing the voice reminder, the driver can ask the passengers to help correct the shoulder belt in the abnormal usage state, so that the shoulder belt can be adjusted in time to ensure the safety of the child riding in the smart safety seat 300. It can also reduce the frequency of the driver or passengers checking the vehicle's center console and ensure the safety of the occupants' driving.

[0063] In a possible implementation, after triggering the automatic reminder mechanism of the smart cockpit domain controller, it may also include: obtaining signal information input by the occupant after receiving the reminder message sent by the automatic reminder mechanism, the signal information including determining to correct the shoulder straps of the shoulder strap restraint module; when the signal information is to determine to correct the shoulder straps of the shoulder strap restraint module, starting navigation identification to find the temporary parking point closest to the current position of the vehicle cabin, and reminding the occupant to go to the temporary parking point to correct the shoulder straps of the shoulder strap restraint module.

[0064] For example, when an occupant is driving the vehicle independently on a highway, it is not convenient for the occupant to adjust the shoulder straps in an abnormal usage state immediately after receiving the reminder message. At this time, the reminder message sent by the smart cockpit domain controller may also include asking the occupant whether to adjust the shoulder straps in an abnormal usage state. After receiving the reminder message sent by the automatic reminder mechanism, the occupant can send signal information to the smart cockpit domain controller through voice input to determine the modification of the shoulder straps. After receiving the signal information, the smart cockpit domain controller starts navigation recognition and searches for the temporary parking point closest to the current position of the vehicle cabin. After finding a suitable temporary parking point, the occupant is reminded to go to the temporary parking point to correct the shoulder straps to ensure the safety of the children.

[0065] In a possible implementation, the locking method of the shoulder straps of the shoulder strap restraint module includes magnetic locking. After triggering the automatic reminder mechanism of the intelligent cockpit domain controller, it also includes: obtaining signal information after the occupant receives the reminder message sent by the automatic reminder mechanism, the signal information includes determining to correct the shoulder straps of the shoulder strap restraint module; controlling the shoulder strap restraint module to lock the shoulder straps of the shoulder strap restraint module by magnetic locking.

[0066] For example, when a passenger is driving the vehicle alone on a highway, in order to ensure the safety of children, the reminder message sent by the smart cockpit domain controller may also include asking the passenger whether to adjust the shoulder straps in abnormal use status. After receiving the reminder message sent by the automatic reminder mechanism, the passenger can send signal information to the smart cockpit domain controller through voice input to determine the modification of the shoulder straps of the shoulder strap restraint module. Since the locking method of the shoulder straps of the shoulder strap restraint module includes magnetic locking, at this time, the smart cockpit domain controller can control the automatic magnetic locking of the shoulder straps of the shoulder strap restraint module according to the signal information, so that the shoulder straps in abnormal use status can be corrected in time.

[0067] In a possible implementation, after correcting the abnormal use status of the shoulder strap, it may also include: controlling the front in-vehicle camera in the vehicle cabin to collect video information of the child in the smart safety seat 300; triggering the automatic tightening control mechanism of the shoulder strap of the smart cabin domain controller according to the video information.

[0068] For example, the automatic tightening control mechanism of the shoulder straps includes a tightening mechanism and a loosening mechanism. The implementation method of triggering the automatic tightening control mechanism of the shoulder straps of the smart cockpit domain controller according to the video information may be: when the video information indicates that the child is in a sleeping state, triggering the tightening mechanism of the smart cockpit domain controller; when the video information indicates that the child is not in a sleeping state, triggering the loosening mechanism of the smart cockpit domain controller.

[0069] The method for determining whether the child is in a sleeping state according to the video information may be as follows: determining whether the child is in a sleeping state according to whether each frame image in the video information changes. If multiple frames of images in the video information change, it is determined that the video information indicates that the child is in a non-sleeping state. If multiple frames of information in the video information do not change, it is determined that the video information indicates that the child is in a sleeping state.

[0070] It can be seen that by adjusting the tightness of the shoulder straps through the automatic tightening control mechanism of the shoulder straps, the safety of the children in the smart safety seat 300 is ensured while improving the comfort of the children in different states when using the shoulder straps.

[0071] S204: If it is detected that the use state of the shoulder belt of the shoulder belt restraint module is a normal use state, the current detection cycle processing is marked as finished, and the operation of the next detection cycle is continued after waiting for a preset time.

[0072] After correcting the shoulder strap in abnormal usage status, the smart cockpit domain controller can further determine whether the usage status of the shoulder strap is normal through the aforementioned method. If the detected usage status is normal, the current detection cycle is marked as ended to avoid the smart cockpit domain controller continuously detecting the shoulder straps of the shoulder strap restraint module, which affects the processing efficiency of the smart cockpit domain controller.

[0073] Furthermore, after marking the completion of the current detection cycle processing, the steps in the aforementioned information processing method can be continued in the next detection cycle after waiting for a preset time, thereby ensuring that the shoulder straps of the smart safety seat 300 are in normal use during the driving of the vehicle, thereby ensuring the safety of children in the smart safety seat 300 during driving.

[0074] In other implementations, step S202 may also include the following embodiments:

[0075] In the third embodiment, the shoulder straps of the shoulder strap restraint module include a left shoulder strap 301 and a right shoulder strap 303, and the left independent contraction component of the shoulder strap restraint module is connected to the left shoulder strap 301, and the right independent contraction component of the shoulder strap restraint module is connected to the right shoulder strap 303; the preset state data includes a plurality of tension data; the use state of the shoulder straps of the shoulder strap restraint module is detected according to the preset state data, including: sending a data request message to the smart safety seat 300, the data request message is used to request a plurality of tension data of the left independent contraction component to which the left shoulder strap 301 belongs, and a plurality of tension data of the right independent contraction component to which the right shoulder strap 303 belongs; receiving a data response message from the smart safety seat 300; parsing the data response message, obtaining a plurality of tension data of the left independent contraction component to which the left shoulder strap 301 belongs, and, Acquire multiple tension data of the right-side independent retracting component to which the right shoulder strap 303 belongs; if analyzing multiple tension data of the left-side independent retracting component of the left shoulder strap 301 determines that the left shoulder strap 301 is in a detached or unfastened state, or analyzing multiple tension data of the right-side independent retracting component to which the right shoulder strap 303 belongs determines that the right shoulder strap 303 is in a detached or unfastened state, then determine that the use state of the shoulder straps of the shoulder strap restraint module is an abnormal use state; if analyzing multiple tension data of the left-side independent retracting component of the left shoulder strap 301 determines that the left shoulder strap 301 is in a fastened and stably constrained state, and, analyzing multiple tension data of the right-side independent retracting component to which the right shoulder strap 303 belongs determines that the right shoulder strap 303 is in a fastened and stably constrained state, then determine that the use state of the shoulder straps of the shoulder strap restraint module is a normal use state.

[0076] Both the left independent retracting assembly and the right independent retracting assembly are provided with tension sensors. For example, the intelligent cockpit domain controller obtains multiple tension data of the left independent retracting assembly to which the left shoulder strap 301 belongs at different time nodes in the current detection cycle and multiple tension data of the right independent retracting assembly to which the right shoulder strap 303 belongs at different time nodes in the current detection cycle, and analyzes whether the multiple tension data of the left independent retracting assembly to which the left shoulder strap 301 belongs and the multiple tension data of the right independent retracting assembly to which the right shoulder strap 303 belongs are both within the preset force range, and if so, determines that the use state of the shoulder strap of the shoulder strap restraint module is a normal use state, otherwise, determines that the use state of the shoulder strap of the shoulder strap restraint module is an abnormal use state.

[0077] For example, in the current detection cycle, the smart cockpit domain controller collects four tension data of the left independent contraction component of the left shoulder strap 301 at t0, t1, t2, and t3 through the smart safety seat 300, and collects four tension data of the right independent contraction component of the right shoulder strap 303 at t0, t1, t2, and t3 through the smart safety seat 300. Among them, t0, t1, t2, and t3 are four time nodes of the total duration of the current detection cycle. If the four tension data of the left independent contraction component of the left shoulder strap 301 are all within the preset force range of the left shoulder strap 301, and the four tension data of the right independent contraction component of the right shoulder strap 303 are also within the preset force range of the right shoulder strap 303, it is determined that the left shoulder strap 301 and the right shoulder strap 303 are both in a fastened and stably constrained state, that is, the use state of the shoulder straps of the shoulder strap restraint module is a normal use state. If one of the four tension data of the left independent contraction component to which the left shoulder strap 301 belongs is smaller than the lower limit value of the preset force range of the left shoulder strap 301, or one of the four tension data of the right independent contraction component to which the right shoulder strap 303 belongs is smaller than the lower limit value of the preset force range of the right shoulder strap 303, then it is determined that the usage status of the shoulder strap of the shoulder strap restraint module is an abnormal usage status.

[0078] It is understandable that the preset force range of the combined belt 305 is different from the preset force range of the single shoulder strap.

[0079] It can be seen that in this embodiment, in the current detection cycle, multiple tension data of the left independent retracting component to which the left shoulder strap 301 belongs and multiple tension data of the right independent retracting component to which the right shoulder strap 303 belongs are collected at multiple different time nodes in the current detection cycle respectively, and then the usage status of the shoulder straps of the shoulder strap restraint module is determined, which can reduce the calculation amount of the intelligent cockpit domain controller and thus improve the detection efficiency.

[0080] In the fourth embodiment, the shoulder strap of the shoulder strap restraint module includes a left shoulder strap 301 and a right shoulder strap 303, and the shoulder strap restraint module is connected to the merged belt 305 with a single integrated retractable component, and the merged belt 305 is a restraint belt that connects the left shoulder strap 301 and the right shoulder strap 303; the preset state data includes a plurality of tension data; the use state of the shoulder strap of the shoulder strap restraint module is detected according to the preset state data, including: sending a data request message to the smart safety seat 300, the data request message is used to request a plurality of tension data of the integrated retractable component to which the merged belt 305 belongs; receiving data from the smart safety seat The data response message of the chair 300 is received; the data response message is parsed to obtain a plurality of tension data of the integrated retracting assembly to which the merging belt 305 belongs; if the plurality of tension data of the integrated retracting assembly to which the merging belt 305 belongs is analyzed to determine that the merging belt 305 is in a detached or unfastened state, then the use state of the shoulder straps of the shoulder strap restraint module is determined to be an abnormal use state; if the plurality of tension data of the integrated retracting assembly to which the merging belt 305 belongs is analyzed to determine that the merging belt 305 is in a fastened and stably constrained state, then the use state of the shoulder straps of the shoulder strap restraint module is determined to be a normal use state.

[0081] It can be understood that the integrated retracting assembly to which the merging belt 305 belongs is equipped with a tension sensor to facilitate the detection of the tension data of the integrated retracting assembly; multiple tension data are obtained at different time nodes in the current detection cycle. Since the left shoulder strap 301 and the right shoulder strap 303 of the merging belt 305 are both connected to the restraint belt, when the use status of the shoulder straps of the shoulder strap restraint module is detected according to multiple tension data, it is only necessary to obtain multiple tension data of the integrated retracting assembly to which the merging belt 305 belongs to determine the use status of the shoulder straps of the shoulder strap restraint module. It can also reduce the amount of calculation of the smart cockpit domain controller and improve the detection efficiency, so that the smart cockpit domain controller can timely remind the shoulder straps of the shoulder strap restraint module in abnormal use status to ensure the safety of children on the smart safety seat 300.

[0082] See also Figure 5 , Figure 5An information processing system 500 for detecting the state of a shoulder belt restraint module in a vehicle cabin is provided in an embodiment of the present application. The information processing system 500 for detecting the state of a shoulder belt restraint module in a vehicle cabin is applied to a smart cabin domain controller in a vehicle cabin, and the smart cabin domain controller is connected to a smart safety seat 300 in communication. The information processing system 500 for detecting the state of a shoulder belt restraint module in a vehicle cabin includes an acquisition module 501, a detection module 502, a trigger module 503, and a marking module 504. The acquisition module 501 is used to acquire preset state data of the vehicle cabin, and the preset state data includes at least one of the following: multiple tension data of the shoulder belt restraint module collected and fed back by the smart safety seat 300, and multiple images of children on the smart safety seat 300 collected and fed back by the front-row in-vehicle camera in the vehicle cabin. The detection module 502 is used to detect the use state of the shoulder belt of the shoulder belt restraint module according to the preset state data, and the use state includes an abnormal use state and a normal use state. The abnormal use state is used to indicate that the shoulder belt is in a detached or unfastened state, and the normal use state is used to indicate that the shoulder belt is in a fastened and stably restrained state. The trigger module 503 is used to trigger the automatic reminder mechanism of the smart cockpit domain controller if the use state is detected to be an abnormal use state, and the automatic reminder mechanism is used to remind the passengers other than children in the smart safety seat 300 in the vehicle cabin that the shoulder belt is in an abnormal use state through voice reminder and / or information display, and remind the passengers to correct the shoulder belt. The marking module 504 is used to mark the end of the current detection cycle processing if the use state is detected to be a normal use state, and wait for a preset time to continue the operation of the next detection cycle.

[0083] In a possible implementation, the shoulder straps of the shoulder strap restraint module include a left shoulder strap 301 and a right shoulder strap 303, and the left independent retractable component of the shoulder strap restraint module is connected to the left shoulder strap 301, and the right independent retractable component of the shoulder strap restraint module is connected to the right shoulder strap 303; the preset state data includes multiple images and multiple tension data, and the detection module 502 is also used to: perform shoulder strap recognition on multiple images of the child on the smart safety seat 300 collected and fed back by the front in-vehicle camera in the vehicle cabin, and obtain a recognition result; if the recognition result is that the multiple images only contain image data of a single shoulder strap, then determine the first detection result of the single shoulder strap according to the image data of the single shoulder strap; if it is detected that the first detection result indicates that the single shoulder strap is Normal usage state, continue to detect the shoulder strap on the other side to obtain a second detection result; if the second detection result indicates that the shoulder strap on the other side is in normal usage state, then the usage state of the shoulder strap of the shoulder strap restraint module is determined to be normal usage state; if the second detection result indicates that the shoulder strap on the other side is in abnormal usage state, then the usage state of the shoulder strap of the shoulder strap restraint module is determined to be abnormal usage state; if the first detection result indicates that the single shoulder strap is in abnormal usage state, then the usage state of the shoulder strap of the shoulder strap restraint module is determined to be abnormal usage state; if the recognition result is that multiple images simultaneously contain image data of the left shoulder strap 301 and the right shoulder strap 303, then the usage state of the shoulder strap of the shoulder strap restraint module is determined according to the image data of the left shoulder strap 301 and the right shoulder strap 303.

[0084] In a possible implementation, when the recognition result is that multiple images only contain image data of the right shoulder strap 303, and the first detection result indicates that the right shoulder strap 303 is in normal use, the detection module 502 is further used to: send a data request message to the smart safety seat 300, the data request message is used to request multiple tension data of the left independent retracting component to which the left shoulder strap 301 belongs; receive a data response message from the smart safety seat 300; parse the data response message to obtain multiple tension data of the left independent retracting component to which the left shoulder strap 301 belongs; if the analysis of the multiple tension data determines that the left shoulder strap 301 is in a detached or unfastened state, then the second detection result is determined to be an abnormal use state; if the analysis of the multiple tension data determines that the left shoulder strap 301 is in a fastened and stably constrained state, then the second detection result is determined to be a normal use state.

[0085] In a possible implementation, when the recognition result is that multiple images only contain image data of the right shoulder strap 303, the detection module 502 is also used to: obtain multiple first matching degrees between the image data of the right shoulder strap 303 and a preset right shoulder strap normal state image; if the multiple first matching degrees are all within a preset matching degree range, then the first detection result is determined to be a normal usage state; if the multiple first matching degrees include a matching degree that is not within the preset matching degree range, then the first detection result is determined to be an abnormal usage state.

[0086] In a possible embodiment, the detection module 502 is also used to: obtain multiple first matching degrees between the image data of the right shoulder strap 303 and a preset normal-state image of the right shoulder strap; obtain multiple second matching degrees between the image data of the left shoulder strap 301 and a preset normal-state image of the left shoulder strap; if the multiple first matching degrees and the multiple second matching degrees are all within the preset matching degree range, it is determined that the usage state of the shoulder straps of the shoulder strap restraint module is a normal usage state; if the multiple first matching degrees and the multiple second matching degrees include one or more that are not within the preset matching degree range, it is determined that the usage state of the shoulder straps of the shoulder strap restraint module is an abnormal usage state.

[0087] In a possible embodiment, the shoulder strap of the shoulder strap restraint module includes a left shoulder strap 301 and a right shoulder strap 303, and the shoulder strap restraint module is connected to a combined belt 305 with a single integrated retractable component, and the combined belt 305 is a restraint belt connecting both the left shoulder strap 301 and the right shoulder strap 303; the preset state data includes multiple images and multiple tension data; the detection module 502 is also used to: perform shoulder strap recognition on multiple images of the child on the smart safety seat 300 collected and fed back by the front in-vehicle camera in the vehicle cabin, and obtain a recognition result; if the recognition result is that the multiple images contain image data of at least one side of the shoulder strap, then determine a first detection result based on the image data; if it is detected that the first detection result indicates that at least one side If the shoulder straps of at least one side are in an abnormal usage state, it is determined that the usage state of the shoulder straps of the shoulder strap restraint module is an abnormal usage state; if it is detected that the first detection result indicates that the shoulder straps on at least one side are in a normal usage state, it is determined that the usage state of the shoulder straps of the shoulder strap restraint module is a normal usage state; if the recognition result is that the multiple images do not contain the left shoulder strap 301 or the right shoulder strap 303, the merged belt 305 is continued to be detected to obtain a second detection result; if the second detection result indicates that the merged belt 305 is in an abnormal usage state, it is determined that the usage state of the shoulder straps of the shoulder strap restraint module is an abnormal usage state; if the second detection result indicates that the merged belt 305 is in a normal usage state, it is determined that the usage state of the shoulder straps of the shoulder strap restraint module is a normal usage state.

[0088] In a possible implementation, the detection module 502 is further used to: send a data request message to the smart safety seat 300, the data request message is used to request multiple tension data of the integrated retraction component to which the merging belt 305 belongs; receive a data response message from the smart safety seat 300; parse the data response message to obtain multiple tension data of the integrated retraction component to which the merging belt 305 belongs; if the analysis of multiple tension data determines that the merging belt 305 is in a detached or unfastened state, then determine that the use state of the shoulder strap of the shoulder strap restraint module is an abnormal use state; if the analysis of multiple tension data determines that the merging belt 305 is in a fastened and stably constrained state, then determine that the use state of the shoulder strap of the shoulder strap restraint module is a normal use state; obtain multiple matching degrees between the image data and a preset normal state image of the shoulder strap; if the multiple matching degrees are all within the preset matching degree range, then determine that the first detection result is a normal use state; if the multiple matching degrees include a matching degree that is not within the preset matching degree range, then determine that the first detection result is an abnormal use state.

[0089] Among them, the implementation of each module of the information processing system 500 for the vehicle cabin based on the shoulder belt restraint module status detection can also correspond to the corresponding description of the processing method embodiment described in any embodiment of the present application.

[0090] See also Figure 6 , Figure 6 A schematic diagram of the composition of an electronic device 600 provided in an embodiment of the present application. The electronic device 600 may be an intelligent vehicle-mounted device of a car. The electronic device 600 may include a processor 601, a memory 602, and a communication interface 603. The processor 601, the memory 602, and the communication interface 603 are connected via a bus 604, the memory 602 is used to store a computer program, the computer program includes program instructions, and the processor 601 is used to execute the program instructions to implement the method described in any embodiment of the present application.

[0091] The memory 602 includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), or a portable read-only memory (CD-ROM), and the memory 602 is used for related computer programs and data. The communication interface 603 is used to receive and send data.

[0092] The processor 601 may be one or more central processing units (CPUs). When the processor 601 is a CPU, the CPU may be a single-core CPU or a multi-core CPU.

[0093] The processor 601 may include one or more processing units, for example, the processing unit may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent components or integrated into one or more processors. In some embodiments, the user equipment may also include one or more processing units. Among them, the controller may generate an operation control signal according to the instruction opcode and the timing signal to complete the control of fetching and executing instructions. In some other embodiments, a memory may also be set in the processing unit for storing instructions and data. Exemplarily, the memory in the processing unit may be a cache memory. The memory may store instructions or data that have just been used or circulated by the processing unit. If the processing unit needs to use the instruction or data again, it can be directly called from the memory. In this way, repeated access is avoided, the waiting time of the processing unit is reduced, and the efficiency of the user equipment in processing data or executing instructions is improved.

[0094] In some embodiments, the processor 601 may include one or more interfaces. The interface may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a SIM card interface and / or a USB interface, etc. Among them, the USB interface is an interface that complies with the USB standard specification, and can specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, etc. The USB interface can be used to connect a charger to charge a user device, and can also be used to transmit data between a user device and a peripheral device. The USB interface can also be used to connect headphones to play audio through the headphones.

[0095] If the electronic device 600 is an intelligent vehicle-mounted device, the processor 601 in the electronic device 600 is used to read the computer program code stored in the memory 602, and control the intelligent cockpit domain controller of the intelligent vehicle-mounted device to perform the following operations:

[0096] Acquire preset state data of the vehicle cabin, the preset state data comprising at least one of the following: a plurality of tension data of the shoulder belt restraint module collected and fed back by the smart safety seat 300, and a plurality of images of the child in the smart safety seat 300 collected and fed back by a front-row in-vehicle camera in the vehicle cabin;

[0097] Detecting the use state of the shoulder belt of the shoulder belt restraint module according to preset state data, the use state includes an abnormal use state and a normal use state, the abnormal use state is used to indicate that the shoulder belt is in a detached or unfastened state, and the normal use state is used to indicate that the shoulder belt is in a fastened and stably restrained state;

[0098] If it is detected that the shoulder belt of the shoulder belt restraint module is in an abnormal use state, the automatic reminder mechanism of the smart cockpit domain controller is triggered, and the automatic reminder mechanism is used to remind the occupants other than children in the smart safety seat 300 in the vehicle cabin that the shoulder belt of the shoulder belt restraint module is in an abnormal use state through voice reminder and / or information display, and remind the occupants to correct the shoulder belt of the shoulder belt restraint module;

[0099] If it is detected that the use state of the shoulder belt of the shoulder belt restraint module is a normal use state, the current detection cycle processing is marked as finished, and the operation of the next detection cycle is continued after waiting for a preset time.

[0100] In a possible implementation, the shoulder strap of the shoulder strap restraint module includes a left shoulder strap 301 and a right shoulder strap 303, and the left independent contraction component of the shoulder strap restraint module is connected to the left shoulder strap 301, and the right independent contraction component of the shoulder strap restraint module is connected to the right shoulder strap 303; the preset state data includes a plurality of images and a plurality of tension data; and detecting the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data includes:

[0101] Perform shoulder strap recognition on multiple images of the child in the smart safety seat 300 collected and fed back by the front in-vehicle camera in the vehicle cabin, and obtain recognition results;

[0102] If the recognition result is that the multiple images only contain image data of a single shoulder strap, determining a first detection result of the single shoulder strap according to the image data of the single shoulder strap;

[0103] If the first detection result indicates that the single shoulder strap is in normal use, continue to detect the other shoulder strap to obtain a second detection result;

[0104] If the second detection result indicates that the other shoulder strap is in a normal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is a normal use state;

[0105] If the second detection result indicates that the other shoulder belt is in an abnormal use state, determining that the use state of the shoulder belt of the shoulder belt restraint module is an abnormal use state;

[0106] If it is detected that the first detection result indicates that the single-side shoulder belt is in an abnormal use state, determining that the use state of the shoulder belt of the shoulder belt restraint module is an abnormal use state;

[0107] If the recognition result is that the multiple images simultaneously contain the image data of the left shoulder strap 301 and the right shoulder strap 303 , the use status of the shoulder straps of the shoulder strap restraint module is determined according to the image data of the left shoulder strap 301 and the right shoulder strap 303 .

[0108] In a possible implementation, when the recognition result is that the multiple images only include image data of the right shoulder strap 303, and the first detection result indicates that the right shoulder strap 303 is in a normal use state, continuing to detect the left shoulder strap 301 to obtain a second detection result includes:

[0109] Sending a data request message to the smart safety seat 300, where the data request message is used to request multiple tension data of the left independent retractable component to which the left shoulder strap 301 belongs;

[0110] Receiving a data response message from the smart safety seat 300;

[0111] Parse the data response message to obtain multiple tension data of the left independent retractable component to which the left shoulder strap 301 belongs;

[0112] If the analysis of the plurality of tension data determines that the left shoulder strap 301 is in a detached or unfastened state, then the second detection result is determined to be an abnormal use state;

[0113] If it is determined by analyzing a plurality of tension data that the left shoulder strap 301 is in a fastened and stably restrained state, then the second detection result is determined to be a normal use state.

[0114] In a possible implementation manner, when the recognition result is that the multiple images only include the image data of the right shoulder strap 303, determining the first detection result of the right shoulder strap 303 according to the image data of the right shoulder strap 303 includes:

[0115] Acquire multiple first matching degrees between the image data of the right shoulder strap 303 and a preset right shoulder strap normal state image;

[0116] If the plurality of first matching degrees are all within the preset matching degree range, determining that the first detection result is a normal use state;

[0117] If the plurality of first matching degrees include a matching degree that is not within the preset matching degree range, the first detection result is determined to be an abnormal usage state.

[0118] In a possible implementation, the shoulder strap of the shoulder strap restraint module includes a left shoulder strap 301 and a right shoulder strap 303, and the shoulder strap restraint module is connected to a combined belt 305 with a single integrated contraction component, and the combined belt 305 is a restraint belt that connects the left shoulder strap 301 and the right shoulder strap 303; the preset state data includes multiple images and multiple tension data; and detecting the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data includes:

[0119] Perform shoulder strap recognition on multiple images of the child in the smart safety seat 300 collected and fed back by the front in-vehicle camera in the vehicle cabin, and obtain recognition results;

[0120] If the recognition result is that the multiple images contain image data of at least one side of the shoulder strap, determining a first detection result according to the image data;

[0121] If it is detected that the first detection result indicates that the shoulder belt on at least one side is in an abnormal use state, determining that the use state of the shoulder belt of the shoulder belt restraint module is an abnormal use state;

[0122] If it is detected that the first detection result indicates that the shoulder strap on at least one side is in a normal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is a normal use state;

[0123] If the recognition result is that the plurality of images do not include the left shoulder strap 301 or the right shoulder strap 303, then continue to detect the merged strap 305 to obtain a second detection result;

[0124] If the second detection result indicates that the combined belt 305 is in an abnormal use state, it is determined that the use state of the shoulder belt of the shoulder belt restraint module is an abnormal use state;

[0125] If the second detection result indicates that the combined belt 305 is in a normal use state, it is determined that the use state of the shoulder belt of the shoulder belt restraint module is a normal use state.

[0126] In a possible implementation, continuing to detect the merged band 305 to obtain a second detection result includes:

[0127] Sending a data request message to the smart safety seat 300, where the data request message is used to request multiple tension data of the integrated retracting assembly to which the merging belt 305 belongs;

[0128] Receiving a data response message from the smart safety seat 300;

[0129] Parse the data response message to obtain multiple tension data of the integrated contraction component to which the merge belt 305 belongs;

[0130] If the analysis of the plurality of tension data determines that the combined belt 305 is in a detached or unfastened state, then the use state of the shoulder belt of the shoulder belt restraint module is determined to be an abnormal use state;

[0131] If the combined belt 305 is determined to be in a fastened and stably restrained state by analyzing the multiple tension data, the use state of the shoulder belt of the shoulder belt restraint module is determined to be a normal use state; and the first detection result is determined according to the image data, including:

[0132] Acquire multiple matching degrees between the image data and a preset normal state image of the shoulder strap;

[0133] If the multiple matching degrees are all within the preset matching degree range, the first detection result is determined to be a normal use state;

[0134] If the plurality of matching degrees include a matching degree that is not within a preset matching degree range, the first detection result is determined to be an abnormal usage state.

[0135] Among them, all relevant contents of each scenario involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here.

[0136] The embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, Figure 2The processing method flow shown is implemented.

[0137] The present application also provides a computer program product. When the computer program product is run on a terminal, Figure 2 The processing method flow shown is implemented.

[0138] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software template corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0139] The embodiment of the present application can divide the electronic device into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0140] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and templates involved are not necessarily required by this application.

[0141] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0142] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0143] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0145] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0146] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0147] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle cabin information processing method based on shoulder belt restraint module state detection, characterized in that: The vehicle comprises: a smart cockpit domain and an autonomous driving domain, the information processing method is applied to a smart cockpit domain controller of the vehicle cockpit, and the smart cockpit domain controller is communicatively connected with a smart safety seat; Wherein, the information processing method includes: Acquiring preset state data of the vehicle cabin, the preset state data comprising at least one of the following: a plurality of tension data of a shoulder belt restraint module collected and fed back by the smart safety seat, and a plurality of images of a child on the smart safety seat collected and fed back by a front-row in-vehicle camera of the vehicle cabin; Detecting the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data, wherein the use state includes an abnormal use state and a normal use state, wherein the abnormal use state is used to indicate that the shoulder strap is in a detached or unfastened state, and the normal use state is used to indicate that the shoulder strap is in a fastened and stably restrained state; If it is detected that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state, the automatic reminder mechanism of the smart cockpit domain controller is triggered, and the automatic reminder mechanism is used to remind the occupants other than the children on the smart safety seat in the vehicle cabin that the shoulder strap of the shoulder strap restraint module is in the abnormal use state through voice reminder and / or information display, and remind the occupants to correct the shoulder strap of the shoulder strap restraint module; If it is detected that the use state of the shoulder belt of the shoulder belt restraint module is a normal use state, the current detection cycle processing is marked as finished, and the operation of the next detection cycle is continued after waiting for a preset time; in, When the shoulder strap of the shoulder strap restraint module includes a left shoulder strap and a right shoulder strap, and the left independent retractable component of the shoulder strap restraint module is connected to the left shoulder strap, and the right independent retractable component of the shoulder strap restraint module is connected to the right shoulder strap; the preset state data includes the multiple images and the multiple tension data; the detecting the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data includes: Performing shoulder strap recognition on the multiple images of the child in the smart safety seat collected and fed back by the front-row in-vehicle camera in the vehicle cabin to obtain a recognition result; If the recognition result is that the multiple images only include image data of a single shoulder strap, determining a first detection result of the single shoulder strap according to the image data of the single shoulder strap; If it is detected that the first detection result indicates that the single shoulder strap is in the normal use state, continue to detect the other shoulder strap to obtain a second detection result; If the second detection result indicates that the other shoulder strap is in the normal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is the normal use state; If the second detection result indicates that the other shoulder strap is in the abnormal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state; If it is detected that the first detection result indicates that the single-side shoulder strap is in the abnormal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state; If the recognition result is that the multiple images simultaneously include the image data of the left shoulder strap and the right shoulder strap, the use status of the shoulder strap of the shoulder strap restraint module is determined according to the image data of the left shoulder strap and the right shoulder strap.

2. The information processing method according to claim 1, characterized in that: When the recognition result is that the multiple images only include image data of the right shoulder strap, and the first detection result indicates that the right shoulder strap is in the normal use state, continuing to detect the left shoulder strap to obtain a second detection result includes: Sending a data request message to the smart safety seat, wherein the data request message is used to request the plurality of tension data of the left independent retractable assembly to which the left shoulder strap belongs; Receiving a data response message from the smart safety seat; Parsing the data response message to obtain the plurality of tension data of the left independent retractable assembly to which the left shoulder strap belongs; If it is determined by analyzing the plurality of tension data that the left shoulder strap is in a detached or unfastened state, then determining the second detection result as the abnormal use state; If it is determined by analyzing the plurality of tension data that the left shoulder strap is in a fastened and stably restrained state, the second detection result is determined to be the normal use state.

3. The information processing method according to claim 2, characterized in that: In a case where the recognition result is that the multiple images only include image data of the right shoulder strap, determining a first detection result of the right shoulder strap according to the image data of the right shoulder strap includes: Acquire a plurality of first matching degrees between the image data of the right shoulder strap and a preset normal state image of the right shoulder strap; If the plurality of first matching degrees are all within the preset matching degree range, determining that the first detection result is the normal use state; If the plurality of first matching degrees include a matching degree that is not within the preset matching degree range, the first detection result is determined to be the abnormal usage state.

4. The information processing method according to claim 1, characterized in that: The determining the use status of the shoulder straps of the shoulder strap restraint module according to the image data of the left shoulder strap and the right shoulder strap comprises: Acquire a plurality of first matching degrees between the image data of the right shoulder strap and a preset normal state image of the right shoulder strap; Acquire multiple second matching degrees between the image data of the left shoulder strap and a preset normal state image of the left shoulder strap; If the plurality of first matching degrees and the plurality of second matching degrees are all within the preset matching degree range, determining that the use state of the shoulder strap of the shoulder strap restraint module is the normal use state; If the plurality of first matching degrees and the plurality of second matching degrees include one or more that are not within the preset matching degree range, it is determined that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state.

5. A vehicle cabin information processing method based on shoulder belt restraint module state detection, characterized in that: The vehicle comprises: a smart cockpit domain and an autonomous driving domain, the information processing method is applied to a smart cockpit domain controller of the vehicle cockpit, and the smart cockpit domain controller is communicatively connected with a smart safety seat; Wherein, the information processing method includes: Acquiring preset state data of the vehicle cabin, the preset state data comprising at least one of the following: a plurality of tension data of a shoulder belt restraint module collected and fed back by the smart safety seat, and a plurality of images of a child on the smart safety seat collected and fed back by a front-row in-vehicle camera of the vehicle cabin; Detecting the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data, wherein the use state includes an abnormal use state and a normal use state, wherein the abnormal use state is used to indicate that the shoulder strap is in a detached or unfastened state, and the normal use state is used to indicate that the shoulder strap is in a fastened and stably restrained state; If it is detected that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state, the automatic reminder mechanism of the smart cockpit domain controller is triggered, and the automatic reminder mechanism is used to remind the occupants other than the children on the smart safety seat in the vehicle cabin that the shoulder strap of the shoulder strap restraint module is in the abnormal use state through voice reminder and / or information display, and remind the occupants to correct the shoulder strap of the shoulder strap restraint module; If it is detected that the use state of the shoulder belt of the shoulder belt restraint module is a normal use state, the current detection cycle processing is marked as finished, and the operation of the next detection cycle is continued after waiting for a preset time; in, When the shoulder strap of the shoulder strap restraint module includes a left shoulder strap and a right shoulder strap, and the shoulder strap restraint module is connected to a combined belt with a single integrated retractable component, the combined belt is a restraint belt that connects both the left shoulder strap and the right shoulder strap; the preset state data includes the multiple images and the multiple tension data; the detecting the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data includes: Performing shoulder strap recognition on a plurality of images of the child in the smart safety seat collected and fed back by a front-row in-vehicle camera in the vehicle cabin to obtain a recognition result; If the recognition result is that the multiple images include image data of at least one side of the shoulder strap, determining a first detection result according to the image data; If it is detected that the first detection result indicates that the shoulder strap on at least one side is in the abnormal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state; If it is detected that the first detection result indicates that the shoulder strap on at least one side is in the normal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is the normal use state; If the recognition result is that the plurality of images do not include the left shoulder strap or the right shoulder strap, continue to detect the combined strap to obtain a second detection result; If the second detection result indicates that the combined belt is in the abnormal use state, determining that the use state of the shoulder belt of the shoulder belt restraint module is the abnormal use state; If the second detection result indicates that the combined belt is in the normal use state, it is determined that the use state of the shoulder belt of the shoulder belt restraint module is the normal use state.

6. An information processing system for a vehicle cabin based on shoulder belt restraint module status detection, characterized in that: The vehicle includes: a smart cockpit domain and an autonomous driving domain, the system is applied to a smart cockpit domain controller of the vehicle cockpit, and the smart cockpit domain controller is communicatively connected with the smart safety seat; Wherein, the information processing system comprises: an acquisition module, configured to acquire preset state data of the vehicle cabin, wherein the preset state data includes at least one of the following: a plurality of tension data of a shoulder belt restraint module collected and fed back by the smart safety seat, and a plurality of images of a child on the smart safety seat collected and fed back by a front-row in-vehicle camera of the vehicle cabin; a detection module, configured to detect the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data, wherein the use state includes an abnormal use state and a normal use state, wherein the abnormal use state is used to indicate that the shoulder strap is in a detached or unfastened state, and the normal use state is used to indicate that the shoulder strap is in a fastened and stably restrained state; a trigger module, configured to trigger an automatic reminder mechanism of the smart cockpit domain controller if it is detected that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state, wherein the automatic reminder mechanism is configured to remind the occupants other than the children on the smart safety seat in the vehicle cockpit that the shoulder strap of the shoulder strap restraint module is in the abnormal use state through voice reminder and / or information display, and to remind the occupants to correct the shoulder strap of the shoulder strap restraint module; A marking module, used for marking the end of the current detection cycle processing if it is detected that the use state of the shoulder strap of the shoulder strap restraint module is the normal use state, and waiting for a preset time to continue the operation of the next detection cycle; in, When the shoulder strap of the shoulder strap restraint module includes a left shoulder strap and a right shoulder strap, and the left independent retractable component of the shoulder strap restraint module is connected to the left shoulder strap, and the right independent retractable component of the shoulder strap restraint module is connected to the right shoulder strap; the preset state data includes the multiple images and the multiple tension data; the detecting the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data includes: Performing shoulder strap recognition on the multiple images of the child in the smart safety seat collected and fed back by the front-row in-vehicle camera in the vehicle cabin to obtain a recognition result; If the recognition result is that the multiple images only include image data of a single shoulder strap, determining a first detection result of the single shoulder strap according to the image data of the single shoulder strap; If it is detected that the first detection result indicates that the single shoulder strap is in the normal use state, continue to detect the other shoulder strap to obtain a second detection result; If the second detection result indicates that the other shoulder strap is in the normal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is the normal use state; If the second detection result indicates that the other shoulder strap is in the abnormal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state; If it is detected that the first detection result indicates that the single-side shoulder strap is in the abnormal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state; If the recognition result is that the multiple images simultaneously include the image data of the left shoulder strap and the right shoulder strap, the use status of the shoulder strap of the shoulder strap restraint module is determined according to the image data of the left shoulder strap and the right shoulder strap.

7. An information processing system for a vehicle cabin based on shoulder belt restraint module status detection, characterized in that: The vehicle includes: a smart cockpit domain and an autonomous driving domain, the system is applied to a smart cockpit domain controller of the vehicle cockpit, and the smart cockpit domain controller is communicatively connected with the smart safety seat; Wherein, the information processing system comprises: an acquisition module, configured to acquire preset state data of the vehicle cabin, wherein the preset state data includes at least one of the following: a plurality of tension data of a shoulder belt restraint module collected and fed back by the smart safety seat, and a plurality of images of a child on the smart safety seat collected and fed back by a front-row in-vehicle camera of the vehicle cabin; a detection module, configured to detect the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data, wherein the use state includes an abnormal use state and a normal use state, wherein the abnormal use state is used to indicate that the shoulder strap is in a detached or unfastened state, and the normal use state is used to indicate that the shoulder strap is in a fastened and stably restrained state; a trigger module, configured to trigger an automatic reminder mechanism of the smart cockpit domain controller if it is detected that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state, wherein the automatic reminder mechanism is configured to remind the occupants other than the children on the smart safety seat in the vehicle cockpit that the shoulder strap of the shoulder strap restraint module is in the abnormal use state through voice reminder and / or information display, and to remind the occupants to correct the shoulder strap of the shoulder strap restraint module; A marking module, used for marking the end of the current detection cycle processing if it is detected that the use state of the shoulder strap of the shoulder strap restraint module is the normal use state, and waiting for a preset time to continue the operation of the next detection cycle; in, When the shoulder strap of the shoulder strap restraint module includes a left shoulder strap and a right shoulder strap, and the shoulder strap restraint module is connected to a combined belt with a single integrated retractable component, the combined belt is a restraint belt that connects both the left shoulder strap and the right shoulder strap; the preset state data includes the multiple images and the multiple tension data; the detecting the use state of the shoulder strap of the shoulder strap restraint module according to the preset state data includes: Performing shoulder strap recognition on the multiple images of the child in the smart safety seat collected and fed back by the front-row in-vehicle camera in the vehicle cabin to obtain a recognition result; If the recognition result is that the multiple images include image data of at least one side of the shoulder strap, determining a first detection result according to the image data; If it is detected that the first detection result indicates that the shoulder strap on at least one side is in the abnormal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is the abnormal use state; If it is detected that the first detection result indicates that the shoulder strap on at least one side is in the normal use state, determining that the use state of the shoulder strap of the shoulder strap restraint module is the normal use state; If the recognition result is that the plurality of images do not include the left shoulder strap or the right shoulder strap, continue to detect the combined strap to obtain a second detection result; If the second detection result indicates that the combined belt is in the abnormal use state, determining that the use state of the shoulder belt of the shoulder belt restraint module is the abnormal use state; If the second detection result indicates that the combined belt is in the normal use state, it is determined that the use state of the shoulder belt of the shoulder belt restraint module is the normal use state.

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the processor and the memory are connected to each other, the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the information processing method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to execute the information processing method according to any one of claims 1 to 5.

10. A computer program product, characterized in that The computer program product comprises program instructions, and when the program instructions are executed by a processor, the processor is caused to execute the information processing method according to any one of claims 1 to 5.

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