CN117731134A - Control method and device of intelligent bed and intelligent bed system - Google Patents

Abstract

The application provides a control method and device of an intelligent bed and an intelligent bed system, wherein the method comprises the following steps: acquiring first acquisition data of a plurality of first object identification sensors, wherein each first object identification sensor is respectively positioned between a telescopic structure of the intelligent bed and the ground; determining the names of objects between the telescopic structure of the intelligent bed and the ground according to the first acquired data to obtain a plurality of first identification object names, and determining a plurality of first target danger levels according to all the first identification object names; at least first alarm information is generated according to one of the highest repetition of all first target risk levels or according to the highest risk level of all first target risk levels. The intelligent bed solves the problem that the safety of the intelligent bed in the lifting process is low in the prior scheme.

Description

Control method and device of intelligent bed and intelligent bed system

Technical Field

The application relates to the technical field of intelligent beds, in particular to a control method and device of an intelligent bed and an intelligent bed system.

Background

The development of the Internet of things enables the intelligent bed to gradually enter the home of consumers for improving sleep quality, and the demand of the intelligent bed is in an ascending trend along with the population aging trend of China.

However, some safety problems may occur during use of the smart bed, for example, if a person's hand is in the middle of a bed head or a bed tail in a raised state (especially a young child, who has not yet consciously descended the bed and presses the hand), and when the remotely controlled bed descends, a certain safety problem may be caused because the person does not know that the middle space is in the middle of the bed.

Namely, the intelligent bed of the prior proposal has lower safety in the lifting process.

Disclosure of Invention

The main object of the present application is to provide a control method, device and intelligent bed system for an intelligent bed, so as to at least solve the problem of low safety of the intelligent bed in the lifting process of the existing scheme.

To achieve the above object, according to one aspect of the present application, there is provided a control method of an intelligent bed, the method comprising: acquiring first acquisition data of a plurality of first object recognition sensors, wherein each first object recognition sensor is respectively positioned between a telescopic structure of the intelligent bed and the ground, each first object recognition sensor is used for recognizing an object between the telescopic structure and the ground, and the first acquisition data comprises the shape of a first recognition object and the size of the first recognition object; determining the names of objects between the telescopic structure of the intelligent bed and the ground according to the first acquired data to obtain a plurality of first identification object names, and determining a plurality of first target risk levels according to all the first identification object names, wherein the first target risk level is one of a plurality of risk levels; and generating at least first alarm information according to one of the first target risk levels with highest repetition degree or according to the highest risk level in the first target risk levels.

Optionally, determining a plurality of first identification object names according to each of the first collected data includes: determining an identification object name corresponding to the first acquisition data in the object name mapping relation according to the first acquisition data and the object name mapping relation, wherein the object name mapping relation is a mapping relation of the shape of an object, the size of the object and the identification object name; and determining the first identification object name as the identification object name corresponding to the first acquisition data in the object name mapping relation.

Optionally, determining a plurality of first identification object names according to each of the first collected data includes: processing the first acquired data by using an object recognition model to obtain output of the object recognition model, wherein the object recognition model is obtained by training by using a plurality of sets of training data, and each set of training data in the plurality of sets of training data comprises data acquired in a historical time period: the shape of the object, the size of the object, and the identification object name corresponding to the shape of the object and the size of the object; determining that the first recognition object name is an output of the object recognition model.

Optionally, generating at least the first alarm information according to one of the first target risk levels with highest repetition or according to the highest risk level of the first target risk levels, including: executing one of the following processing modes according to the highest repetition degree of all the first target risk levels or according to the highest risk level of all the first target risk levels: the intelligent bed lifting system comprises a first processing mode, a second processing mode and a third processing mode, wherein the first processing mode is used for generating first alarm information, the second processing mode is used for generating the first alarm information and stopping the lifting action of the intelligent bed, the intelligent bed is controlled to continue to perform the lifting action under the condition that a lifting recovery instruction is received, and the third processing mode is used for stopping the lifting action of the intelligent bed and broadcasting dangerous objects in a voice mode, so that the lifting action cannot be performed.

Optionally, the risk level includes a first risk level, a second risk level, and a third risk level with sequentially decreasing risk levels, and according to one of the highest repetition rates of all the first target risk levels, one of the following processing modes is executed: the first, second, and third processing modes include: executing the third processing mode under the condition that one of the repetition rates of all the first target risk levels is the third risk level; executing the second processing mode under the condition that one of the first target risk levels with the highest repetition degree is the second risk level; and executing the first processing mode under the condition that one of all the first target risk levels with the highest repetition degree is the first risk level.

Optionally, the risk level includes a first risk level, a second risk level, and a third risk level with sequentially decreasing risk levels, and one of the following processing modes is performed according to the highest risk level among all the first target risk levels: the first, second, and third processing modes include: executing the third processing mode when the highest risk level among all the first target risk levels is the third risk level; executing the second processing mode when the highest risk level in all the first target risk levels is the second risk level; and executing the first processing mode when the highest risk level in all the first target risk levels is the first risk level.

Optionally, after generating at least the first alarm information according to one of the highest repetition of all the first target risk levels or according to the highest risk level of all the first target risk levels, the method further includes: acquiring second acquired data of a plurality of second object identification sensors, wherein each second object identification sensor is respectively positioned on a mattress of the intelligent bed, each second object identification sensor is used for identifying an object between the telescopic structure and the ground, and the second acquired data comprises a shape of a second identification object and a size of the second identification object; determining the names of objects on the mattress of the intelligent bed according to the second acquired data to obtain a plurality of second identification object names, and determining a plurality of second target risk levels according to all the second identification object names, wherein the second identification object names and the second target risk levels are in one-to-one correspondence, and the second target risk level is one of the risk levels; and generating at least second alarm information according to the highest repetition degree of all the second target danger levels or according to the highest danger level of all the second target danger levels.

Optionally, the risk level includes a first risk level, a second risk level, and a third risk level with sequentially decreasing risk levels, and determining a plurality of first target risk levels according to all the first identification object names includes: the first recognition object name is one of the following: under the conditions of a human body, a sharp object and an open flame object, determining the first target risk level as the first risk level; determining that the first target hazard level is the second hazard level in the case where the first identified object name is an iron; and determining that the first target risk level is the third risk level under the condition that the first identification object name is an uncapped bottle structure.

According to another aspect of the present application, there is provided a control device of an intelligent bed, the device comprising:

the intelligent bed comprises a first acquisition unit, a first recognition unit and a second acquisition unit, wherein the first acquisition unit is used for acquiring first acquisition data of a plurality of first object recognition sensors, each first object recognition sensor is respectively positioned between a telescopic structure of the intelligent bed and the ground, each first object recognition sensor is used for recognizing an object between the telescopic structure and the ground, and the first acquisition data comprises the shape of a first recognition object and the size of the first recognition object;

The first determining unit is used for determining the names of objects between the telescopic structure of the intelligent bed and the ground according to the first acquired data to obtain a plurality of first identification object names, and determining a plurality of first target danger levels according to all the first identification object names, wherein the first target danger level is one of the plurality of danger levels;

and the first processing unit is used for generating at least first alarm information according to one of the first target risk levels with highest repeatability or according to the highest risk level in the first target risk levels.

According to another aspect of the present application, there is provided a smart bed system, comprising: the intelligent bed comprises a controller and an intelligent bed, wherein the controller is communicated with the intelligent bed, and the controller is used for executing any control method of the intelligent bed.

By means of the technical scheme, the names of the objects between the telescopic structure of the intelligent bed and the ground are identified through the data of the objects between the telescopic structure of the intelligent bed and the ground by the plurality of first object identification sensors, corresponding first target dangerous grades are identified, and finally, according to the highest repetition degree of all the first target dangerous grades, or according to the highest dangerous grade of all the first target dangerous grades, at least first alarm information is generated, so that the purpose of alarming is achieved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:

FIG. 1 shows a flow diagram of a method of controlling a smart bed provided in accordance with an embodiment of the present application;

FIG. 2 shows a flow diagram of another smart bed control method provided in accordance with an embodiment of the present application;

fig. 3 shows a block diagram of a control device of a smart bed according to an embodiment of the present application.

Detailed Description

It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the present application described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

As described in the background art, some safety problems may occur in the use process of the intelligent bed, for example, when the head of a bed or the tail of a bed is in a raised state, if someone's hand is in a middle empty position (especially a child, the hand is pressed when the bed is not lowered), and when the remote control bed is lowered, the person is not beside the bed, and does not know that the middle empty space is provided with the hand, so that a certain safety problem may be caused, and in order to solve the problem that the safety of the intelligent bed in the lifting process of the existing scheme is lower, the embodiment of the present application provides a control method, a device and an intelligent bed system of the intelligent bed.

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

In this embodiment, a control method of a smart bed is provided, and it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different from that herein.

Fig. 1 is a schematic flow chart of a control method of a smart bed according to an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:

step S101, acquiring first acquisition data of a plurality of first object recognition sensors, wherein each first object recognition sensor is respectively positioned between a telescopic structure of the intelligent bed and the ground, each first object recognition sensor is used for recognizing an object between the telescopic structure and the ground, and the first acquisition data comprises the shape of a first recognition object and the size of the first recognition object;

specifically, the intelligent bed is a conventional automatic telescopic bed capable of stretching up and down, automatic telescopic rods (namely telescopic structures) are arranged between a mattress of the intelligent bed and the surrounding structures of the bed, the telescopic rods can drag the mattress to move upwards or downwards, and the range between the automatic telescopic rods and the ground is the range between the telescopic structures of the intelligent bed and the ground;

The first object recognition sensor may be a millimeter wave sensor, an infrared sensor, an ultrasonic sensor, a microwave radar sensor, a human body proximity sensor, etc., and taking the microwave radar sensor as an example, the shape, the size, the position and even penetration of some objects may be recognized for detection.

Step S102, determining the names of objects between the telescopic structure of the intelligent bed and the ground according to the first acquired data to obtain a plurality of first identification object names, and determining a plurality of first target danger levels according to all the first identification object names, wherein the first target danger levels are one of the danger levels;

specifically, since the accuracy of the identification of the optical voucher sensor is low, the plurality of first identification object names are obtained by the acquisition results of the plurality of first object identification sensors (the acquisition results obtained by the difference of the acquisition angles of the sensor installation are not necessarily the same), and then a plurality of first target risk levels are determined according to all the first identification object names, wherein one first target risk level comprises the names of a plurality of objects.

According to each first collected data, the names of the objects between the telescopic structure of the intelligent bed and the ground are determined, so that a plurality of first identification object names are obtained, and two specific embodiments are provided:

The first embodiment is: determining an identification object name corresponding to the first acquisition data in the object name mapping relation according to the first acquisition data and the object name mapping relation, wherein the object name mapping relation is a mapping relation of the shape of an object, the size of the object and the identification object name; and determining the first identification object name as the identification object name corresponding to the first acquired data in the object name mapping relation.

Specifically, the object name mapping relationship is set to be the shape of the object, the size of the object (the size can be represented by the volume of the object) and the mapping relationship of the identified object name, so that the identified object name corresponding to the first acquired data is conveniently found out from the object name mapping relationship according to the first acquired data.

The second embodiment is: processing the first acquired data by using an object recognition model to obtain an output of the object recognition model, wherein the object recognition model is obtained by training by using a plurality of sets of training data, and each set of training data in the plurality of sets of training data comprises acquired in a historical time period: the shape of the object, the size of the object, and the identification object name corresponding to the shape of the object and the size of the object; determining that the first recognition object name is an output of the object recognition model.

Specifically, compared with the mapping relation, the object name is accurately identified by adopting the neural network model technology of the object identification model, the first acquired data is used as the input of the object identification model, the object identification model processes the first acquired data, and the first identification object name is output.

The risk level includes a first risk level, a second risk level, and a third risk level, which are sequentially reduced in risk level;

in step S102, determining a plurality of first target risk levels according to all the first identification object names includes: the first recognition object name is one of the following: under the conditions of a human body, a sharp object and an open flame object, determining the first target risk level as the first risk level; determining that the first target risk level is the second risk level when the first identified object name is an iron; and determining that the first target risk level is the third risk level when the first identified object is a bottle structure with an uncapped cap.

Specifically, the human body is considered to be an infant, so that the human body needs to be one of the most dangerous objects, the sharp object can be a needle line, the object with thorns, the open fire object can be an alcohol lamp in combustion or other objects in combustion, the bottle body structure without the cover can be a beverage bottle without the cover and a cosmetic water bottle without the cover, and the iron is dangerous, but is safer than the human body, the sharp object and the open fire object.

Step S103, at least generating first alarm information according to one of the highest repetition of all the first target risk levels or according to the highest risk level of all the first target risk levels.

Specifically, the risk levels include a first risk level, a second risk level, and a third risk level, the risk levels of which decrease in order, for example, four first target levels are the first risk level, the second risk level, and the third risk level, respectively, and since there are two first risk levels and only one other risk level, the repetition of the first risk level is the highest, the subsequent steps are performed according to the first risk level.

In the above steps, the names of the objects located between the telescopic structure of the intelligent bed and the ground are identified through the data of the objects located between the telescopic structure of the intelligent bed and the ground by the plurality of first object identification sensors, the corresponding first target dangerous grades are identified, and finally, at least first alarm information is generated according to one of the first target dangerous grades with the highest repeatability or according to the highest dangerous grade of the first target dangerous grades, so that the purpose of alarming is achieved.

Step S103, namely, generating at least a first alarm message according to one of the highest repetition rates of all the first target risk levels or according to the highest risk level of all the first target risk levels, including:

executing one of the following processing modes according to the highest repetition degree of all the first target risk levels or according to the highest risk level of all the first target risk levels: a first processing mode, a second processing mode and a third processing mode, wherein the first processing mode is used for generating first alarm information, the second processing mode is used for generating the first alarm information and stopping the lifting action of the intelligent bed, and when a lifting recovery instruction is received, the intelligent bed is controlled to continue to perform the lifting action, and the third processing mode is used for stopping the lifting action of the intelligent bed and broadcasting dangerous goods in a voice mode, so that the lifting action cannot be performed.

Specifically, the first alarm information can be that an alarm is adopted to alarm, a user can get up as soon as possible after hearing the alarm sound, the lifting action of the intelligent bed is stopped when the alarm is adopted to alarm in the second processing mode, the intelligent bed can be continuously controlled to lift only when the user presses a button for continuously executing the lifting action, the third processing mode broadcasts in a voice mode that dangerous goods exist, the alarm can whistle when the lifting action cannot be carried out, and the third processing mode only is restarted or the intelligent bed is reset to carry out the lifting action.

There are two other specific embodiments:

the first embodiment is: executing the third processing mode when one of the first target risk levels having the highest repetition is the third risk level; executing the second processing mode when one of the first target risk levels having the highest repetition is the second risk level; the first processing method is executed when one of the first target risk levels having the highest repetition is the first risk level.

Specifically, for example, the four first target levels are the first risk level, the second risk level, and the third risk level, respectively, and since there are two first risk levels and only one other risk level, the repetition of the first risk level is the highest, the third processing mode is executed, and if the repetition of the second risk level is the highest, the second processing mode is executed, and the third risk level is the same and will not be described herein.

The second embodiment is: executing the third processing mode when the highest risk level among all the first target risk levels is the third risk level; executing the second processing mode when the highest risk level among all the first target risk levels is the second risk level; the first processing mode is executed when the highest risk level among all the first target risk levels is the first risk level.

Specifically, for example, the four first target levels are the first risk level, the second risk level, and the third risk level, respectively, the first risk level is the highest risk level, the third processing mode is performed, for example, the four first target levels are the second risk level, the third risk level, the second risk level, and the third risk level, respectively, the second risk level is the highest risk level, the second processing mode is performed, for example, the four first target levels are the third risk level, and the first processing mode is performed.

After step S103, after generating at least the first alarm information according to one of the highest repetition of all the first target risk levels or according to the highest risk level of all the first target risk levels, the method further includes: acquiring second acquired data of a plurality of second object recognition sensors, wherein each second object recognition sensor is respectively positioned on a mattress of the intelligent bed, each second object recognition sensor is used for recognizing an object between the telescopic structure and the ground, and the second acquired data comprises a shape of a second recognition object and a size of the second recognition object; determining the names of objects on the mattress of the intelligent bed according to the second acquired data to obtain a plurality of second identification object names, and determining a plurality of second target risk levels according to all the second identification object names, wherein the second identification object names and the second target risk levels are in one-to-one correspondence, and the second target risk level is one of the risk levels; and generating at least second alarm information according to one of the highest repetition of all the second target risk levels or according to the highest risk level of all the second target risk levels.

Specifically, the process is the same as the steps S101, S102, and S103, and will not be described in detail herein, since each of the second object recognition sensors is located on the mattress of the intelligent bed, respectively, it is possible to detect the dangerous object located on the mattress of the intelligent bed on the basis of detecting the dangerous object located between the telescopic structure of the intelligent bed and the ground, respectively, and prevent the dangerous object on the mattress from falling down when the mattress is lifted or lowered, which is not known to the user. Finally, one of the following processing modes may be executed according to the highest repetition degree of all the second target risk levels, or according to the highest risk level of all the second target risk levels: a fourth processing mode, a fifth processing mode and a sixth processing mode, wherein the fourth processing mode at least generates second alarm information, adopts different alarms to alarm, the fifth processing mode is to generate the second alarm information and stop the lifting action of the intelligent bed, and when receiving a lifting recovery instruction, the intelligent bed is controlled to continue the lifting action, the sixth processing mode is to stop the lifting action of the intelligent bed and broadcast in a voice mode that dangerous goods exist, so that the lifting action cannot be performed, and the dangerous goods exist on a broadcast mattress can be distinguished from the third processing mode.

In order to enable those skilled in the art to more clearly understand the technical solutions of the present application, the implementation process of the control method of the smart bed of the present application will be described in detail below with reference to specific embodiments.

The embodiment relates to a specific control method of an intelligent bed, as shown in fig. 2, comprising the following steps:

step S1: acquiring first acquisition data of a plurality of first object recognition sensors, wherein each first object recognition sensor is respectively positioned between a telescopic structure of the intelligent bed and the ground, each first object recognition sensor is used for recognizing an object between the telescopic structure and the ground, and the first acquisition data comprises the shape of the first recognition object and the size of the first recognition object;

step S2: processing the first acquired data by using an object recognition model to obtain an output of the object recognition model, wherein the object recognition model is obtained by training by using a plurality of sets of training data, and each set of training data in the plurality of sets of training data comprises data acquired in a historical time period: the shape of the object, the size of the object, and the identification object name corresponding to the shape of the object and the size of the object; determining the first recognition object name as the output of the object recognition model;

Step S3: the risk level includes a first risk level, a second risk level, and a third risk level, the risk levels decreasing in order, and the first recognition object name is one of: under the conditions of a human body, a sharp object and an open flame object, determining a first target risk level as a first risk level; in the case where the first identified object name is an iron, determining that the first target hazard level is a second hazard level; determining that the first target risk level is a third risk level in the case of a first identified object named uncapped bottle structure;

step S4: executing a third processing mode under the condition that the highest risk level in all the first target risk levels is a third risk level; executing a second processing mode under the condition that the highest risk level in all the first target risk levels is the second risk level; executing a first processing mode under the condition that the highest risk level in all the first target risk levels is the first risk level;

the first processing mode is to generate first alarm information, the second processing mode is to generate first alarm information and stop the lifting action of the intelligent bed, and under the condition that a lifting recovery instruction is received, the intelligent bed is controlled to continue to perform the lifting action, and the third processing mode is to stop the lifting action of the intelligent bed and broadcast dangerous goods in a voice mode, so that the lifting action cannot be performed.

The object data between the telescopic structure of the intelligent bed and the ground are used for identifying the names of the objects between the telescopic structure of the intelligent bed and the ground, identifying the corresponding first target danger level, and finally generating at least first alarm information according to one of the first target danger levels with the highest repetition degree or according to the highest danger level of the first target danger levels, so that the purpose of alarming is achieved.

It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.

The embodiment of the application also provides a control device of the intelligent bed, and the control device of the intelligent bed can be used for executing the control method for the intelligent bed. The device is used for realizing the above embodiments and preferred embodiments, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

The following describes a control device of an intelligent bed provided in an embodiment of the present application.

Fig. 3 is a block diagram of a control device of a smart bed according to an embodiment of the present application. As shown in fig. 3, the apparatus includes:

a first acquiring unit 31 configured to acquire first acquired data of a plurality of first object recognition sensors, each of the first object recognition sensors being located between a telescopic structure of the smart bed and a ground surface, each of the first object recognition sensors being configured to recognize an object between the telescopic structure and the ground surface, the first acquired data including a shape of a first recognition object and a size of the first recognition object;

a first determining unit 32 configured to determine names of objects located between the telescopic structure of the smart bed and the ground according to the first collected data, obtain a plurality of first identification object names, and determine a plurality of first target risk levels according to all the first identification object names, where the first target risk level is one of the plurality of risk levels;

the first processing unit 33 is configured to generate at least the first alarm information according to one of the first target risk levels having the highest repetition or according to the highest risk level of the first target risk levels.

In the device, the names of the objects between the telescopic structure of the intelligent bed and the ground are identified through the data of the objects between the telescopic structure of the intelligent bed and the ground by a plurality of first object identification sensors, the corresponding first target danger levels are identified, and finally, at least first alarm information is generated according to one of the first target danger levels with the highest repetition degree or according to the highest danger level in all the first target danger levels, so that the purpose of alarming is achieved.

In one embodiment of the present application, the first determining unit includes a first determining module and a second determining module, where the first determining module is configured to determine, according to the first collected data and an object name mapping relationship, an identified object name corresponding to the first collected data in the object name mapping relationship, where the object name mapping relationship is a mapping relationship of a shape of an object, a size of the object, and the identified object name; the second determining module is configured to determine the first identified object name as the identified object name corresponding to the first collected data in the object name mapping relationship.

In an embodiment of the present application, the first determining unit includes a first processing module and a third determining module, where the first processing module is configured to process the first collected data by using an object recognition model to obtain an output of the object recognition model, where the object recognition model is obtained by training using multiple sets of training data, and each set of training data in the multiple sets of training data includes acquired in a historical period of time: the shape of the object, the size of the object, and the identification object name corresponding to the shape of the object and the size of the object; the third determining module is used for determining that the first identification object name is the output of the object identification model.

In an embodiment of the present application, the first processing unit includes a second processing module, where the second processing module is configured to execute, according to one of the repetition rates of all the first target risk levels that is highest, or according to the highest risk level of all the first target risk levels that is highest, a processing manner of one of the following: a first processing mode for generating first alarm information, a second processing mode for generating the first alarm information and stopping the lifting operation of the intelligent bed, and controlling the intelligent bed to continue the lifting operation when a lifting recovery instruction is received, and a third processing mode for stopping the lifting operation of the intelligent bed and broadcasting dangerous goods in the form of voice, so that the lifting operation cannot be performed

In an embodiment of the present application, the risk level includes a first risk level, a second risk level, and a third risk level, where the risk levels sequentially decrease, and the first processing unit includes a third processing module, a fourth processing module, and a fifth processing module, where the third processing module is configured to execute the third processing manner when one of all the first target risk levels has the highest repetition degree is the third risk level; the fourth processing module is configured to execute the second processing mode when one of the repetition rates of all the first target risk levels is the second risk level; and the fifth processing module is used for executing the first processing mode when all the first target risk level with the highest repetition degree is the first risk level.

In an embodiment of the present application, the risk level includes a first risk level, a second risk level, and a third risk level, in which risk levels are sequentially reduced, and the first processing unit includes a sixth processing module, a seventh processing module, and an eighth processing module, where the sixth processing module is configured to execute the third processing mode when the highest risk level among all the first target risk levels is the third risk level; the seventh processing module is configured to execute the second processing mode when the highest risk level among all the first target risk levels is the second risk level; the eighth processing module is configured to execute the first processing method when the highest risk level among all the first target risk levels is the first risk level.

In one embodiment of the present application, the apparatus further includes a second acquisition unit, a second determination unit, and a second processing unit, where after generating at least first alarm information according to one of the highest repetition rates of all the first target risk levels or according to the highest risk level of all the first target risk levels, the second acquisition unit is configured to acquire second acquired data of a plurality of second object recognition sensors, each of the second object recognition sensors being located on a mattress of the smart bed, respectively, and each of the second object recognition sensors being configured to recognize an object between the telescopic structure and the ground, the second acquired data including a shape of a second recognition object and a size of the second recognition object; the second determining unit is used for determining the names of the objects on the mattress of the intelligent bed according to the second acquired data to obtain a plurality of second identification object names, and determining a plurality of second target risk levels according to all the second identification object names, wherein the second identification object names and the second target risk levels are in one-to-one correspondence, and the second target risk level is one of the risk levels; the second processing unit is configured to generate at least second alarm information according to one of the highest repetition rates of all the second target risk levels, or according to the highest risk level of all the second target risk levels.

In an embodiment of the present application, the risk level includes a first risk level, a second risk level, and a third risk level, where the risk level sequentially decreases, and the first determining unit includes a fourth determining module, a fifth determining module, and a sixth determining module, where the fourth determining module is configured to determine, in the first identified object name, one of the following: under the conditions of a human body, a sharp object and an open flame object, determining the first target risk level as the first risk level; a fifth determining module for determining that the first target risk level is the second risk level in case the first identification object name is an iron; the sixth determining module is configured to determine the first target risk level as the third risk level when the first identification object name is an uncapped bottle structure.

The control device of the intelligent bed comprises a processor and a memory, wherein the first acquisition unit, the first determination unit, the first processing unit and the like are all stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions. The modules are all located in the same processor; alternatively, the above modules may be located in different processors in any combination.

The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can be provided with one or more than one kernel, and the problem of low safety of the intelligent bed in the lifting process of the existing scheme is solved by adjusting kernel parameters.

The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.

The embodiment of the invention provides a computer readable storage medium, which comprises a stored program, wherein the program is used for controlling equipment where the computer readable storage medium is located to execute the control method of the intelligent bed.

The embodiment of the invention provides a processor, which is used for running a program, wherein the control method of the intelligent bed is executed when the program runs.

The embodiment of the invention provides equipment, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, wherein the processor realizes at least the following steps when executing the program: acquiring first acquisition data of a plurality of first object recognition sensors, wherein each first object recognition sensor is respectively positioned between a telescopic structure of the intelligent bed and the ground, each first object recognition sensor is used for recognizing an object between the telescopic structure and the ground, and the first acquisition data comprises the shape of a first recognition object and the size of the first recognition object; determining the names of objects positioned between the telescopic structure of the intelligent bed and the ground according to the first acquired data to obtain a plurality of first identification object names, and determining a plurality of first target risk levels according to all the first identification object names, wherein the first target risk level is one of a plurality of risk levels; and generating at least first alarm information according to one of the highest repetition of all the first target risk levels or according to the highest risk level of all the first target risk levels. The device herein may be a server, PC, PAD, cell phone, etc.

The present application also provides a computer program product adapted to perform a program initialized with at least the following method steps when executed on a data processing device: acquiring first acquisition data of a plurality of first object recognition sensors, wherein each first object recognition sensor is respectively positioned between a telescopic structure of the intelligent bed and the ground, each first object recognition sensor is used for recognizing an object between the telescopic structure and the ground, and the first acquisition data comprises the shape of a first recognition object and the size of the first recognition object; determining the names of objects positioned between the telescopic structure of the intelligent bed and the ground according to the first acquired data to obtain a plurality of first identification object names, and determining a plurality of first target risk levels according to all the first identification object names, wherein the first target risk level is one of a plurality of risk levels; and generating at least first alarm information according to one of the highest repetition of all the first target risk levels or according to the highest risk level of all the first target risk levels.

The application also provides an intelligent bed system, the intelligent bed system includes: the intelligent bed comprises a controller and an intelligent bed, wherein the controller is communicated with the intelligent bed, and the controller is used for executing any control method of the intelligent bed. The object data between the telescopic structure of the intelligent bed and the ground are used for identifying the names of the objects between the telescopic structure of the intelligent bed and the ground, identifying the corresponding first target danger level, and finally generating at least first alarm information according to one of the first target danger levels with the highest repetition degree or according to the highest danger level of the first target danger levels, so that the purpose of alarming is achieved.

It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

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

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

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

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

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.

From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

1) According to the intelligent bed control method, the names of the objects between the telescopic structures of the intelligent bed and the ground are identified through the data of the objects between the telescopic structures of the intelligent bed and the ground by the plurality of first object identification sensors, the corresponding first target danger levels are identified, and finally, at least first alarm information is generated according to one of the first target danger levels with the highest repeatability or according to the highest danger level of the first target danger levels, so that the purpose of alarming is achieved.

2) According to the intelligent bed control device, the names of objects between the telescopic structure of the intelligent bed and the ground are identified through the data of the objects between the telescopic structure of the intelligent bed and the ground by the plurality of first object identification sensors, corresponding first target dangerous grades are identified, and finally, according to one of the first target dangerous grades, which is the highest in repeatability, or according to the highest dangerous grade of the first target dangerous grades, at least first alarm information is generated, so that the purpose of alarming is achieved, and the accuracy of object identification is improved due to the fact that the acquired data of the plurality of first object identification sensors are considered, and the problem that the intelligent bed in the prior scheme is lower in safety in the lifting process is solved.

The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims (10)

1. A control method of an intelligent bed, comprising:

Acquiring first acquisition data of a plurality of first object recognition sensors, wherein each first object recognition sensor is respectively positioned between a telescopic structure of the intelligent bed and the ground, each first object recognition sensor is used for recognizing an object between the telescopic structure and the ground, and the first acquisition data comprises the shape of a first recognition object and the size of the first recognition object;

determining the names of objects between the telescopic structure of the intelligent bed and the ground according to the first acquired data to obtain a plurality of first identification object names, and determining a plurality of first target risk levels according to all the first identification object names, wherein the first target risk level is one of a plurality of risk levels;

and generating at least first alarm information according to one of the first target risk levels with highest repetition degree or according to the highest risk level in the first target risk levels.

2. The method of claim 1, wherein determining a plurality of first recognition object names from each of the first collected data comprises:

determining an identification object name corresponding to the first acquisition data in the object name mapping relation according to the first acquisition data and the object name mapping relation, wherein the object name mapping relation is a mapping relation of the shape of an object, the size of the object and the identification object name;

And determining the first identification object name as the identification object name corresponding to the first acquisition data in the object name mapping relation.

3. The method of claim 1, wherein determining a plurality of first recognition object names from each of the first collected data comprises:

processing the first acquired data by using an object recognition model to obtain output of the object recognition model, wherein the object recognition model is obtained by training by using a plurality of sets of training data, and each set of training data in the plurality of sets of training data comprises data acquired in a historical time period: the shape of the object, the size of the object, and the identification object name corresponding to the shape of the object and the size of the object;

determining that the first recognition object name is an output of the object recognition model.

4. The method of claim 1, wherein generating at least a first alert message based on the highest repetition of all of the first target risk levels or based on the highest risk level of all of the first target risk levels comprises:

executing one of the following processing modes according to the highest repetition degree of all the first target risk levels or according to the highest risk level of all the first target risk levels: the intelligent bed lifting system comprises a first processing mode, a second processing mode and a third processing mode, wherein the first processing mode is used for generating first alarm information, the second processing mode is used for generating the first alarm information and stopping the lifting action of the intelligent bed, the intelligent bed is controlled to continue to perform the lifting action under the condition that a lifting recovery instruction is received, and the third processing mode is used for stopping the lifting action of the intelligent bed and broadcasting dangerous objects in a voice mode, so that the lifting action cannot be performed.

5. The method of claim 4, wherein the risk level includes a first risk level, a second risk level, and a third risk level having sequentially decreasing risk levels, and wherein, based on a highest repetition of all of the first target risk levels, one of the following processing methods is performed: the first, second, and third processing modes include:

executing the third processing mode under the condition that one of the repetition rates of all the first target risk levels is the third risk level;

executing the second processing mode under the condition that one of the first target risk levels with the highest repetition degree is the second risk level;

and executing the first processing mode under the condition that one of all the first target risk levels with the highest repetition degree is the first risk level.

6. The method of claim 4, wherein the risk levels include a first risk level, a second risk level, and a third risk level having sequentially decreasing risk levels, and wherein one of the following is performed based on a highest risk level among all of the first target risk levels: the first, second, and third processing modes include:

Executing the third processing mode when the highest risk level among all the first target risk levels is the third risk level;

executing the second processing mode when the highest risk level in all the first target risk levels is the second risk level;

and executing the first processing mode when the highest risk level in all the first target risk levels is the first risk level.

7. The method of claim 1, wherein after generating at least a first alert message based on one of the highest repetition of all of the first target risk levels or based on the highest risk level of all of the first target risk levels, the method further comprises:

acquiring second acquired data of a plurality of second object identification sensors, wherein each second object identification sensor is respectively positioned on a mattress of the intelligent bed, each second object identification sensor is used for identifying an object between the telescopic structure and the ground, and the second acquired data comprises a shape of a second identification object and a size of the second identification object;

Determining the names of objects on the mattress of the intelligent bed according to the second acquired data to obtain a plurality of second identification object names, and determining a plurality of second target risk levels according to all the second identification object names, wherein the second identification object names and the second target risk levels are in one-to-one correspondence, and the second target risk level is one of the risk levels;

and generating at least second alarm information according to the highest repetition degree of all the second target danger levels or according to the highest danger level of all the second target danger levels.

8. The method according to any one of claims 1 to 7, wherein the risk levels include a first risk level, a second risk level, and a third risk level, the risk levels decreasing in order, a plurality of first target risk levels being determined from all of the first recognition object names, the method further comprising:

the first recognition object name is one of the following: under the conditions of a human body, a sharp object and an open flame object, determining the first target risk level as the first risk level;

Determining that the first target hazard level is the second hazard level in the case where the first identified object name is an iron;

and determining that the first target risk level is the third risk level under the condition that the first identification object name is an uncapped bottle structure.

9. A control device for an intelligent bed, comprising:

the intelligent bed comprises a first acquisition unit, a first recognition unit and a second acquisition unit, wherein the first acquisition unit is used for acquiring first acquisition data of a plurality of first object recognition sensors, each first object recognition sensor is respectively positioned between a telescopic structure of the intelligent bed and the ground, each first object recognition sensor is used for recognizing an object between the telescopic structure and the ground, and the first acquisition data comprises the shape of a first recognition object and the size of the first recognition object;

the first determining unit is used for determining the names of objects between the telescopic structure of the intelligent bed and the ground according to the first acquired data to obtain a plurality of first identification object names, and determining a plurality of first target danger levels according to all the first identification object names, wherein the first target danger level is one of the plurality of danger levels;

And the first processing unit is used for generating at least first alarm information according to one of the first target risk levels with highest repeatability or according to the highest risk level in the first target risk levels.

10. A smart bed system, comprising: a controller and a smart bed, the controller being in communication with the smart bed, the controller being for performing the method of controlling a smart bed according to any one of claims 1 to 8.