CN114304980A - Airbag adjusting method, airbag adjusting device, electronic apparatus, and medium - Google Patents
Airbag adjusting method, airbag adjusting device, electronic apparatus, and medium Download PDFInfo
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Abstract
The disclosure provides an air bag adjusting method, an air bag adjusting device, electronic equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical field of intelligent cabins. The specific implementation scheme is as follows: determining the current bearing pressure of an air bag module in the target equipment at the current moment; predicting target state information of the airbag module under the current bearing pressure according to the historical bearing pressure of the airbag module at the historical moment and the historical state information of the airbag module under the historical bearing pressure; and adjusting the airbag module according to the target state information. The air bag module self-adaptive adjusting device has the advantages that the effect of self-adaptively adjusting the air bag module according to the current bearing pressure of the air bag module is realized, the adjusting diversity of the adjustment of the air bag module is expanded, and the adjusting precision of the air bag module is improved.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to an airbag adjusting method, an apparatus, an electronic device, and a medium.
Background
Along with the development of society, the proportion of sitting time in people's daily life and operational environment is bigger and bigger, therefore intelligent seat is born at the same time. The intelligent seat has additional functions of heating, ventilation, massage and the like, and also has a memory adjusting function.
At present, the memory adjustment function of the intelligent seat is mostly realized by setting a fixed adjustment path.
Disclosure of Invention
The present disclosure provides a method, apparatus, electronic device, and medium for improving accuracy of airbag adjustment.
According to an aspect of the present disclosure, there is provided an airbag adjusting method including:
determining the current bearing pressure of an air bag module in the target equipment at the current moment;
predicting target state information of the airbag module under the current bearing pressure according to the historical bearing pressure of the airbag module at the historical moment and the historical state information of the airbag module under the historical bearing pressure;
and adjusting the airbag module according to the target state information.
According to another aspect of the present disclosure, there is provided an airbag adjusting apparatus including:
the bearing pressure determining module is used for determining the current bearing pressure of an air bag module in the target equipment at the current moment;
the state prediction module is used for predicting target state information of the air bag module under the current bearing pressure according to the historical bearing pressure of the air bag module at the historical moment and the historical state information of the air bag module under the historical bearing pressure;
and the air bag adjusting module is used for adjusting the air bag module according to the target state information.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of the present disclosure.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, performs the method of any one of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a flow chart of some disclosed airbag adjustment methods according to embodiments of the present disclosure;
FIG. 2A is a flow chart of another disclosed airbag adjustment method according to an embodiment of the present disclosure;
FIG. 2B is a schematic view of a scenario of some airbag module adjustments disclosed in accordance with an embodiment of the present disclosure;
FIG. 3 is a flow chart of another disclosed airbag adjustment method according to an embodiment of the present disclosure;
FIG. 4 is a schematic illustration of the construction of some of the disclosed airbag adjustment devices according to embodiments of the present disclosure;
fig. 5 is a block diagram of an electronic device for implementing the airbag adjusting method disclosed in the embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the research and development process, the applicant finds that the existing intelligent seat adjustment can be divided into two modes of manual adjustment and automatic adjustment, and the adjustment of the air bag module of the intelligent seat is taken as an example for explanation.
Firstly, manual adjustment. When a user sits on the intelligent seat, the user actively adjusts the internal air pressure of each air bag module of the intelligent seat according to the current sitting posture of the user, for example, the internal air pressure is increased or reduced, and the like, so that the adjusted air bag module can enable the user to reach the most comfortable state.
And secondly, automatic adjustment. The user carries out memory setting in the intelligent seat with the optimized internal air pressure of each air bag module adjusted in a manual adjusting mode in advance. When a user sits on the intelligent seat again, any one of the memorized preferable internal air pressures is directly selected, and the internal air pressures of the air bag modules are automatically adjusted correspondingly according to the preferable internal air pressure by the intelligent seat.
However, the applicant's research shows that the two modes have the following disadvantages:
the first method requires the user to actively adjust, and thus takes a long time and labor cost. The second way, although it achieves automatic adjustment, can only select from several memorized preferred internal air pressures, the adjustment of the airbag module is less versatile, and if the user changes the sitting posture or is a different user, the memorized preferred internal air pressures may no longer be comfortable for the user, resulting in a lower accuracy of the adjustment of the airbag module.
Fig. 1 is a flow chart of some airbag adjustment methods disclosed according to embodiments of the present disclosure, which may be applicable to the case of automatic adjustment of an airbag module in a target device. The method of the present embodiment may be performed by the airbag adjusting apparatus disclosed in the embodiments of the present disclosure, which may be implemented by software and/or hardware, and may be integrated on any electronic device with computing capability.
As shown in fig. 1, the airbag adjusting method disclosed in the present embodiment may include:
s101, determining the current bearing pressure of an air bag module in the target equipment at the current moment.
The target devices include, but are not limited to, smart seats, smart sofas, smart mattresses and other smart devices loaded with airbag modules. The airbag module is disposed in a contact surface where the target device makes contact with the user, and for example, the target device is an intelligent seat, the airbag module may be disposed in a cushion contact surface, a backrest contact surface, and an armrest contact surface. The pressure sensor is arranged in the air bag module, and when a user contacts with the target equipment to extrude the air bag module, the air bag module can detect the bearing pressure. The current time represents a time when the user makes contact with the target device in the current posture. The current bearing pressure indicates a bearing pressure of the airbag module when the current user makes contact with the target device in the current posture.
In an embodiment, a user makes contact with a target device, and the airbag module sends the detected bearing pressure to the control system in real time, where the control system may be disposed in the target device or in the cloud server, and this embodiment does not limit the specific form of the control system. The number of the airbag modules can be one or more, and when the number of the airbag modules is one, a single numerical bearing pressure is sent to the control system; when the number of the airbag modules is multiple, a data set of the pressure borne by all the airbag modules is sent to the control system.
When the control system determines that the received bearing pressure does not change within the preset time threshold, it is determined that the contact posture of the user with the target device is fixed, for example, the user sits on the smart seat in a fixed sitting posture. The pressure to be received by each airbag module at this time is then used as the current pressure to be received at the current time.
In another embodiment, after the contact gesture of the user with the target device is fixed, the user actively sends a confirmation signal to the control system, for example, clicks a confirmation button in a display interface of the target device, or performs a voice instruction to the target device, such as "i am seated", and the target device generates a confirmation signal accordingly and sends the confirmation signal to the control system. The control system takes the pressure of each airbag module at this time as the current pressure at the current time.
Optionally, when the number of the airbag modules is multiple, the current bearing pressure of each airbag module is represented in the form of a pressure distribution diagram, where the pressure distribution diagram includes the position of each airbag module and the corresponding current bearing pressure.
The current bearing pressure of the air bag module in the target equipment at the current moment is determined, so that a data base is laid for subsequently predicting the target state information of the air bag module.
S102, predicting target state information of the airbag module under the current bearing pressure according to the historical bearing pressure of the airbag module at the historical moment and the historical state information of the airbag module under the historical bearing pressure.
The historical pressure is the pressure received by the airbag module when the contact posture of the user with the target device is fixed in the historical time. The historical state information is obtained by adjusting the state information of the airbag module according to the supporting comfort degree of the airbag module to the user under the historical bearing pressure. The state information of the airbag module includes, but is not limited to, position information of the airbag module and air pressure information of the airbag module.
In one embodiment, the control system controls the state information of the airbag modules to be in default state information when the target device is not in contact with the user, for example, the air pressure information of all the airbag modules is the same, or the air pressure of the airbag modules in the same contact area is the same, such as the air pressure of the airbag module in the hip contact area is the same, the air pressure of the airbag module in the back contact area is the same, and the air pressure of the airbag module in the waist contact area is the same.
When the control system determines that the user makes contact with the target device and determines that the contact posture of the user with the target device is fixed, the bearing pressure of the air bag module at the moment is recorded as historical bearing pressure. And the control system can monitor the state information of the air bag module in real time, and when the user adjusts the default state information of the air bag module according to the requirement of the user on the comfort level, the control system records the adjusted state information as historical state information when the adjusted state information of the air bag module meets the requirement on the comfort level. Further, the control system establishes a correlation between the recorded historical bearing pressure and the historical state information.
And training the model to be trained by taking the historical bearing pressure and the historical state information associated with the historical bearing pressure as a training data set to obtain a state prediction model, wherein the type of the state prediction model is a neural network model. The trained state prediction model learns the state information of the airbag module, which is considered by a user to meet the comfort requirement, of the airbag module under different bearing pressures. The state prediction model thus has the ability to predict airbag module state information.
And inputting the determined current bearing pressure of the airbag module at the current moment into the state prediction model, so that the state prediction model predicts the target state information which is considered by a user to meet the comfort requirement under the current bearing pressure of the airbag module, and further obtains the target state information according to the output of the state prediction model.
The target state information of the air bag module under the current bearing pressure is predicted according to the historical bearing pressure of the air bag module at the historical moment and the historical state information of the air bag module under the historical bearing pressure, so that the effect of adaptively predicting the target state information according to the current bearing pressure of the air bag module is achieved.
S103, adjusting the air bag module according to the target state information.
In one embodiment, the control system determines current state information of the airbag module at the current time and determines a state adjustment parameter, such as a position adjustment parameter and/or an air pressure adjustment parameter, based on a difference between the target state information and the current state information. And then the air bag module is adjusted according to the state adjusting parameters, such as increasing air pressure, decreasing air pressure, moving position and the like.
According to the method, the current bearing pressure of the air bag module in the target equipment at the current moment is determined, the target state information of the air bag module under the current bearing pressure is predicted according to the historical bearing pressure of the air bag module at the historical moment and the historical state information of the air bag module under the historical bearing pressure, and then the air bag module is adjusted according to the target state information, manual adjustment is not needed, the efficiency is improved, and the labor cost is saved; moreover, the effect of self-adaptively adjusting the air bag module according to the current bearing pressure of the air bag module is realized, the adjustment diversity of the adjustment of the air bag module is expanded, the target state information of the air bag module meeting the comfort requirement can be predicted even if different sitting postures and different users exist, and the adjustment accuracy of the air bag module is improved.
Fig. 2A is a flow chart of another airbag adjusting method disclosed according to an embodiment of the present disclosure, which is further optimized and expanded based on the above technical solution, and can be combined with the above various alternative embodiments.
As shown in fig. 2A, the airbag adjusting method disclosed in the present embodiment may include:
s201, determining the current bearing pressure of an air bag module in the target device at the current moment.
S202, predicting target state information of the airbag module under the current bearing pressure according to the current bearing pressure and the incidence relation between the historical bearing pressure and the historical state information; wherein the target state information includes target position information and/or target air pressure information.
The target position information indicates the position of the airbag module meeting the comfort requirement under the current bearing pressure, and can be represented by three-dimensional coordinates. The target air pressure information indicates an internal air pressure value at which the airbag module meets the comfort requirement under the current bearing pressure.
In one embodiment, a state prediction model is constructed according to historical bearing pressure and historical state information, and target state information of the air bag module under the current bearing pressure is predicted according to the current bearing pressure and the state prediction model.
Optionally, S202 includes:
inputting the current bearing pressure into a state prediction model, and predicting target state information of the airbag module under the current bearing pressure; the state prediction model is obtained by training a model to be trained by taking historical bearing pressure as input and historical state information associated with the historical bearing pressure as output.
In one implementation mode, historical bearing pressure is used as an input training sample and is input into a model to be trained, the model to be trained outputs prediction state information, historical state information related to the historical bearing pressure is further used as an output training sample, a loss value between the historical state information and the prediction state information is calculated by adopting a preset loss function, model weight in the model to be trained is adjusted by adopting a back propagation method according to the loss value, and the trained state prediction model is finally obtained.
And inputting the current bearing pressure into the trained state prediction model, so that the state prediction model predicts the target state information which is considered by the user to meet the comfort requirement under the current bearing pressure by the air bag module, and further acquiring the target state information according to the output of the state prediction model.
The current bearing pressure is input into the state prediction model, the target state information of the airbag module under the current bearing pressure is predicted, and the state prediction model is obtained by training according to the historical bearing pressure and the historical state information and has strong prediction capability, so that the time required for predicting the target state information is shortened, and the accuracy of target state information prediction is improved.
S203, determining the current position information and the current air pressure information of the air bag module at the current moment.
In one embodiment, the control system determines current position information and current air pressure information of the airbag module at the current time.
Optionally, the current position information and the current air pressure information may be preset default position information and default air pressure information, or historical position information and historical air pressure information adjusted for a previous user, respectively.
S204, determining position adjusting parameters according to the target position information and the current position information, and determining air pressure adjusting parameters according to the target air pressure information and the current air pressure information.
In one embodiment, a position difference is calculated from the target position information and the current position information, and the position difference is used as a position adjustment parameter. For example, assuming that the current position information is (a1, B1, C1) and the target position information is (a2, B2, C2), then (a2-a1, B2-B1, C2-C1) is used as the position adjustment parameter.
In another embodiment, an air pressure difference value is calculated according to the target air pressure information and the current air pressure information, and the air pressure difference value is used as an air pressure adjusting parameter. Exemplarily, assuming that the current air pressure information is 1.0bar and the target air pressure information is 1.5bar, 1.5bar-1.0 bar-0.5 bar is used as the air pressure adjusting parameter; assuming that the current air pressure information is 1.0bar and the target air pressure information is 0.5bar, 0.5bar-1.0 bar-0.5 bar is used as the air pressure adjustment parameter. That is, when the air pressure adjustment parameter is greater than zero, it indicates that the internal air pressure is increased, and when the air pressure adjustment parameter is less than zero, it indicates that the internal air pressure is decreased.
S205, adjusting the air bag module according to the position adjusting parameter and the air pressure adjusting parameter.
In one embodiment, the control system controls the position adjustment unit in the airbag module to perform position adjustment according to the position adjustment parameter, and controls the air pressure adjustment unit in the airbag module to perform air pressure adjustment according to the air pressure adjustment parameter.
Fig. 2B is a schematic diagram of a scenario of some airbag module adjustments disclosed according to an embodiment of the present disclosure, as shown in fig. 2B, 20 denotes a target device, 21 denotes an airbag module, 22 denotes a position adjustment unit in the airbag module, 23 denotes an air pressure adjustment unit in the airbag module, 24 denotes a control system, and the control system 24 is communicatively connected with each airbag module 21. The control system 24 controls the position adjusting unit 22 in the airbag module 21 to perform position adjustment according to the position adjusting parameter, and controls the air pressure adjusting unit 23 in the airbag module 21 to perform air pressure adjustment according to the air pressure adjusting parameter. The position adjusting unit 22 drives the air pressure adjusting unit 23 to move together, and the air pressure adjusting unit 23 is composed of an air bag with variable air pressure.
According to the method, the target state information of the air bag module under the current bearing pressure is predicted according to the current bearing pressure and the incidence relation between the historical bearing pressure and the historical state information, so that the effect of predicting the target state information under the current bearing pressure based on the historical bearing pressure and the historical state information is achieved, and the target state information meets the requirement of a user on the comfort level; the current position information and the current air pressure information of the air bag module at the current moment are determined, the position adjusting parameter is determined according to the target position information and the current position information, the air pressure adjusting parameter is determined according to the target air pressure information and the current air pressure information, and then the air bag module is adjusted according to the position adjusting parameter and the air pressure adjusting parameter, so that the air bag module is adjusted in two dimensions of position and air pressure, the adjustment diversity of the air bag module is improved, and the user experience is improved.
Fig. 3 is a flow chart of other airbag adjusting methods disclosed according to the embodiments of the present disclosure, which are further optimized and expanded based on the above technical solutions, and can be combined with the above various alternative embodiments.
As shown in fig. 3, the airbag adjusting method disclosed in the present embodiment may include:
s301, determining the current bearing pressure of an airbag module in the target device at the current moment.
S302, predicting the target scene information of the airbag module at the current bearing pressure according to the current bearing pressure and the incidence relation between the historical bearing pressure and the historical scene information.
The scene information represents a scene where the user is located in the target device, and the target device is taken as an intelligent seat as an example, and the scene information includes, but is not limited to, a work scene, a rest scene, a driving scene, and the like. Different scene information shows that the contact posture of the user and the target device is different, and the corresponding airbag module can bear different pressure. For example, under the scene of work, the user's position of sitting usually can be very upright, and under the scene of rest, the user's position of sitting usually can be more lazy scattered, consequently under scene of work and scene of rest, the gasbag module also has different bearing pressure.
In one embodiment, the historical bearing pressure is labeled, and scene information of the airbag module at each historical bearing pressure is determined as historical scene information related to the historical bearing pressure. And training the model to be trained by taking the historical bearing pressure and the historical scene information associated with the historical bearing pressure as a training data set to obtain a scene classification model, wherein the type of the scene classification model is a neural network model. The trained scene classification model learns the scene information of the user under different bearing pressures of the airbag module, so that the scene classification model has the capability of predicting the scene information of the user under different bearing pressures of the airbag module. And inputting the current bearing pressure into a scene classification model to determine target scene information.
Optionally, S303 includes:
inputting the current bearing pressure into a scene classification model, and predicting target scene information of the airbag module at the current bearing pressure; the scene classification model is obtained by training a model to be trained by taking historical bearing pressure as input and historical scene information associated with the historical bearing pressure as output.
In one implementation mode, historical bearing pressure is used as an input training sample and is input into a model to be trained, the model to be trained outputs prediction scene information, historical scene information related to the historical bearing pressure is further used as an output training sample, a loss value between the historical scene information and the prediction scene information is calculated by adopting a preset loss function, model weights in the model to be trained are adjusted by adopting a back propagation method according to the loss value, and finally a trained scene classification model is obtained.
Inputting the current bearing pressure into the trained scene classification model, and allowing the scene classification model to predict candidate scene information where the user is likely to be under the current bearing pressure of the air bag module, and further selecting the candidate scene information with the maximum probability value as the target scene information.
The current bearing pressure is input into the scene classification model, and the target scene information of the airbag module at the current bearing pressure is predicted, so that a data base is laid for predicting the target state information according to the target scene information.
S303, determining the target state information of the airbag module under the current bearing pressure according to the target scene information and the incidence relation between the historical scene information and the historical state information.
In one embodiment, the association between the historical situation information and the historical state information is determined according to the association between the historical bearing pressure and the historical state information and the association between the historical bearing pressure and the historical situation information. And matching the target scene information with the incidence relation between the historical scene information and the historical state information, and determining the target state information according to the matching result.
Optionally, S303 includes the following steps a and B:
A. and determining auxiliary state information associated with the target scene information according to the target scene information and the association relationship between the historical scene information and the historical state information.
In one embodiment, the target scene information is matched with the incidence relation between the historical scene information and the historical state information, and the historical state information associated with the historical scene information matched with the target scene information is used as the auxiliary state information associated with the target scene information.
For example, assuming that the target scene information is "work scene", the historical state information associated with the historical scene information "work scene" includes "state information a", "state information B", and "state information C", the historical state information associated with the historical scene information "rest scene" includes "state information D", "state information E", and "state information F", the historical state information associated with the historical scene information "driving scene" includes "state information J", "state information H", and "state information I", and the auxiliary state information associated with the target scene information "work scene" is "state information a", "state information B", and "state information C".
B. Recommending according to the selected times of each auxiliary state information, and taking the auxiliary state information as the target state information of the airbag module under the current bearing pressure according to the selection operation of any auxiliary state information.
In one embodiment, the target device recommends the auxiliary state information associated with the target scene information to the user for the user to select from the auxiliary state information to determine the auxiliary state information meeting the comfort requirement of the user.
In an actual scene, the target device displays the auxiliary state information list to a user through the display interface, the user can perform pre-selection operation on any auxiliary state information, such as touch pre-selection or voice pre-selection, and the control system pre-adjusts the airbag module according to the auxiliary state information pre-selected by the user, so that the user feels the comfort of the airbag module under different auxiliary state information. And when the user confirms that the air bag module best meets the self comfort requirement under certain auxiliary state information, selecting the auxiliary state information to serve as target state information, and adjusting the air bag module by the control system according to the target state information. In addition, when the number of times of selection of the target state information is increased by one so that the subsequent target device performs the recommendation of the assist state information, it is preferable to recommend the assist state information having the larger number of times of selection, for example, recommend the assist state information having the top three times of selection.
The auxiliary state information related to the target scene information is determined according to the target scene information and the incidence relation between the historical scene information and the historical state information, the auxiliary state information is recommended according to the selected times of the auxiliary state information, the auxiliary state information is used as the target state information of the air bag module under the current bearing pressure according to the selection operation of any auxiliary state information, the effect of recommending the auxiliary state information with better comfort degree to a user based on the selected times is achieved, and therefore the finally selected target state information is enabled to better meet the comfort degree requirement of the user.
S304, adjusting the air bag module according to the target state information.
According to the method and the device, the target scene information of the air bag module under the current bearing pressure is predicted according to the current bearing pressure and the incidence relation between the historical bearing pressure and the historical scene information, and the target state information of the air bag module under the current bearing pressure is determined according to the target scene information and the incidence relation between the historical scene information and the historical state information, so that the corresponding target state information is determined according to different target scene information, the air bag module in the target state information can better meet the comfort requirement of a user in the target scene, the adjustment accuracy of the air bag module is improved, and the user experience is improved.
Fig. 4 is a schematic structural diagram of some airbag adjusting devices disclosed according to the embodiments of the present disclosure, which can be applied to the case of automatically adjusting an airbag module in a target device. The device of the embodiment can be implemented by software and/or hardware, and can be integrated on any electronic equipment with computing capability.
As shown in fig. 4, the airbag adjusting apparatus 40 disclosed in the present embodiment may include a withstand pressure determining module 41, a state predicting module 42, and an airbag adjusting module 43, wherein:
a bearing pressure determining module 41, configured to determine a current bearing pressure of the airbag module in the target device at a current moment;
a state prediction module 42, configured to predict target state information of the airbag module under the current bearing pressure according to a historical bearing pressure of the airbag module at a historical time and historical state information of the airbag module under the historical bearing pressure;
and the air bag adjusting module 43 is used for adjusting the air bag module according to the target state information.
Optionally, the state prediction module 42 is specifically configured to:
and predicting the target state information of the airbag module under the current bearing pressure according to the current bearing pressure and the incidence relation between the historical bearing pressure and the historical state information.
Optionally, the state prediction module 42 is further specifically configured to:
inputting the current bearing pressure into a state prediction model, and predicting target state information of the airbag module under the current bearing pressure;
the state prediction model is obtained by training a model to be trained by taking historical bearing pressure as input and historical state information associated with the historical bearing pressure as output.
Optionally, the state prediction module 42 is further specifically configured to:
predicting target scene information of the airbag module at the current bearing pressure according to the current bearing pressure and the incidence relation between the historical bearing pressure and the historical scene information;
and determining the target state information of the airbag module under the current bearing pressure according to the target scene information and the incidence relation between the historical scene information and the historical state information.
Optionally, the state prediction module 42 is further specifically configured to:
inputting the current bearing pressure into a scene classification model, and predicting target scene information of the airbag module at the current bearing pressure;
the scene classification model is obtained by training a model to be trained by taking historical bearing pressure as input and historical scene information associated with the historical bearing pressure as output.
Optionally, the state prediction module 42 is further specifically configured to:
determining auxiliary state information associated with the target scene information according to the target scene information and the association relationship between the historical scene information and the historical state information;
recommending according to the selected times of each auxiliary state information, and taking the auxiliary state information as the target state information of the airbag module under the current bearing pressure according to the selection operation of any auxiliary state information.
Optionally, the target state information includes target position information and/or target air pressure information;
the airbag adjusting module 43 is specifically configured to:
determining current position information and current air pressure information of the air bag module at the current moment;
determining a position adjusting parameter according to the target position information and the current position information, and determining an air pressure adjusting parameter according to the target air pressure information and the current air pressure information;
and adjusting the air bag module according to the position adjusting parameter and the air pressure adjusting parameter.
The airbag adjusting device 40 disclosed in the embodiment of the disclosure can execute the airbag adjusting method disclosed in the embodiment of the disclosure, and has corresponding functional modules and beneficial effects of the executing method. Reference may be made to the description in the method embodiments of the present disclosure for details that are not explicitly described in this embodiment.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 5 illustrates a schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 500 comprises a computing unit 501 which may perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 501 executes the respective methods and processes described above, such as the air bag adjustment method. For example, in some embodiments, the air bag adjustment method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the airbag adjustment method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the airbag adjustment method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (17)
1. An airbag adjustment method comprising:
determining the current bearing pressure of an air bag module in the target equipment at the current moment;
predicting target state information of the airbag module under the current bearing pressure according to the historical bearing pressure of the airbag module at the historical moment and the historical state information of the airbag module under the historical bearing pressure;
and adjusting the airbag module according to the target state information.
2. The method of claim 1, wherein predicting the target state information of the airbag module at the current bearing pressure based on the historical bearing pressure of the airbag module at the historical moment and the historical state information of the airbag module at the historical bearing pressure comprises:
and predicting the target state information of the airbag module under the current bearing pressure according to the current bearing pressure and the incidence relation between the historical bearing pressure and the historical state information.
3. The method of claim 2, wherein predicting the target state information of the airbag module at the current applied pressure based on the current applied pressure and the correlation between the historical applied pressure and the historical state information comprises:
inputting the current bearing pressure into a state prediction model, and predicting target state information of the airbag module under the current bearing pressure;
the state prediction model is obtained by training a model to be trained by taking historical bearing pressure as input and historical state information associated with the historical bearing pressure as output.
4. The method of claim 2, wherein predicting the target state information of the airbag module at the current applied pressure based on the current applied pressure and the correlation between the historical applied pressure and the historical state information comprises:
predicting target scene information of the airbag module at the current bearing pressure according to the current bearing pressure and the incidence relation between the historical bearing pressure and the historical scene information;
and determining the target state information of the airbag module under the current bearing pressure according to the target scene information and the incidence relation between the historical scene information and the historical state information.
5. The method of claim 4, wherein the predicting the target scene information of the airbag module at the current bearing pressure according to the current bearing pressure and the correlation between the historical bearing pressure and the historical scene information comprises:
inputting the current bearing pressure into a scene classification model, and predicting target scene information of the airbag module at the current bearing pressure;
the scene classification model is obtained by training a model to be trained by taking historical bearing pressure as input and historical scene information associated with the historical bearing pressure as output.
6. The method of claim 4, wherein the determining the target state information of the airbag module at the current applied pressure according to the target scene information and the association relationship between the historical scene information and the historical state information comprises:
determining auxiliary state information associated with the target scene information according to the target scene information and the association relationship between the historical scene information and the historical state information;
recommending according to the selected times of each auxiliary state information, and taking the auxiliary state information as the target state information of the airbag module under the current bearing pressure according to the selection operation of any auxiliary state information.
7. The method of any of claims 1-6, wherein the target state information includes target location information and/or target barometric pressure information;
the adjusting the airbag module according to the target state information includes:
determining current position information and current air pressure information of the air bag module at the current moment;
determining a position adjusting parameter according to the target position information and the current position information, and determining an air pressure adjusting parameter according to the target air pressure information and the current air pressure information;
and adjusting the air bag module according to the position adjusting parameter and the air pressure adjusting parameter.
8. An airbag adjustment assembly comprising:
the bearing pressure determining module is used for determining the current bearing pressure of an air bag module in the target equipment at the current moment;
the state prediction module is used for predicting target state information of the air bag module under the current bearing pressure according to the historical bearing pressure of the air bag module at the historical moment and the historical state information of the air bag module under the historical bearing pressure;
and the air bag adjusting module is used for adjusting the air bag module according to the target state information.
9. The apparatus of claim 8, wherein the state prediction module is specifically configured to:
and predicting the target state information of the airbag module under the current bearing pressure according to the current bearing pressure and the incidence relation between the historical bearing pressure and the historical state information.
10. The apparatus of claim 9, wherein the state prediction module is further specifically configured to:
inputting the current bearing pressure into a state prediction model, and predicting target state information of the airbag module under the current bearing pressure;
the state prediction model is obtained by training a model to be trained by taking historical bearing pressure as input and historical state information associated with the historical bearing pressure as output.
11. The apparatus of claim 9, wherein the state prediction module is further specifically configured to:
predicting target scene information of the airbag module at the current bearing pressure according to the current bearing pressure and the incidence relation between the historical bearing pressure and the historical scene information;
and determining the target state information of the airbag module under the current bearing pressure according to the target scene information and the incidence relation between the historical scene information and the historical state information.
12. The apparatus of claim 11, wherein the state prediction module is further specifically configured to:
inputting the current bearing pressure into a scene classification model, and predicting target scene information of the airbag module at the current bearing pressure;
the scene classification model is obtained by training a model to be trained by taking historical bearing pressure as input and historical scene information associated with the historical bearing pressure as output.
13. The apparatus of claim 11, wherein the state prediction module is further specifically configured to:
determining auxiliary state information associated with the target scene information according to the target scene information and the association relationship between the historical scene information and the historical state information;
recommending according to the selected times of each auxiliary state information, and taking the auxiliary state information as the target state information of the airbag module under the current bearing pressure according to the selection operation of any auxiliary state information.
14. The apparatus of claims 8-13, wherein the target state information comprises target location information and/or target barometric pressure information;
the airbag adjustment module is specifically configured to:
determining current position information and current air pressure information of the air bag module at the current moment;
determining a position adjusting parameter according to the target position information and the current position information, and determining an air pressure adjusting parameter according to the target air pressure information and the current air pressure information;
and adjusting the air bag module according to the position adjusting parameter and the air pressure adjusting parameter.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
17. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-7.
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