An intelligent electric bed adjusting method and system based on artificial intelligence

Abstract

The application provides an intelligent electric bed adjusting method and system based on artificial intelligence, and belongs to the technical field of artificial intelligence, which is used to prolong the time of deep sleep. The method comprises the following steps: a control device collects physiological state information of a monitored object in M continuous time periods, a total of M pieces of physiological state information, M being an integer greater than 2, and the monitored object being located on an intelligent electric bed; the control device processes the M pieces of physiological state information through a neural network model to determine whether the monitored object enters a deep sleep state; and if the monitored object enters the deep sleep state, the control device adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, and in particular to an intelligent electric bed adjusting method and system based on artificial intelligence. BACKGROUND

[0002] Deep sleep, also known as slow wave sleep, is one of the most important stages in the sleep cycle. In this stage, the brain waves show high-amplitude low-frequency delta waves, and the cerebral cortex cells are in a fully rested state, which plays a crucial role in eliminating fatigue, restoring energy, and immune resistance to diseases. Studies have shown that during deep sleep, the body's growth hormone secretion increases, muscles, bones and other tissues are repaired, and metabolic waste is effectively removed, thereby promoting the recovery of physical fatigue.

[0003] Therefore, how to prolong the time of deep sleep is a current research problem. SUMMARY

[0004] The embodiments of the present application provide an intelligent electric bed adjusting method and system based on artificial intelligence to prolong the time of deep sleep.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] In a first aspect, an intelligent electric bed adjusting method based on artificial intelligence is provided, applied to a control device, and the method comprises: the control device collects physiological state information of a monitored object in M consecutive time periods, a total of M physiological state information, M being an integer greater than 2, and the monitored object is located on the intelligent electric bed; the control device processes the M physiological state information through a neural network model to determine whether the monitored object enters a deep sleep state; if the monitored object enters the deep sleep state, the control device adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object.

[0007] Optionally, the control device processes the M physiological state information through the neural network model to determine whether the monitored object enters the deep sleep state, comprising: the control device converts the M physiological state information into a physiological state matrix; the control device processes the physiological state matrix through the neural network model to determine whether the monitored object enters the deep sleep state.

[0008] Optionally, the control device converts the M physiological state information into a physiological state matrix, comprising: the control device converts each of the M physiological state information into a vector set, a total of M vector sets, each vector set in the M vector sets containing a parameter corresponding to a row in an initial matrix; the control device performs a row association operation on the initial matrix to obtain the physiological state matrix.

[0009] Optionally, for the i-th physiological state information of the M physiological state information, i is any integer from 1 to M, the i-th physiological state information includes K physiological state parameters collected at K time points in the i-th time period of the M time periods, a total of K physiological state parameters, K is an integer greater than 2, and any one of the K physiological state parameters includes a plurality of physiological state values; accordingly, the control device converts each of the M physiological state information into a vector set, a total of M vector sets, including: the control device maps the plurality of physiological state values included in each of the K physiological state parameters into a vector, a total of K vectors, the K vectors are the i-th vector set of the M vector sets, the M vector sets are obtained when i traverses from 1 to M, the initial matrix is an M*K matrix, M is the number of rows of the initial matrix, and K is the number of columns of the initial matrix; accordingly, the control device performs a row association operation on the initial matrix to obtain a physiological state matrix, including: the control device randomly extracts J1 vectors from the K vectors of the 1st row of the initial matrix, and randomly inserts the J1 vectors into the K vectors of the 2nd row of the initial matrix; the control device randomly extracts J2 vectors from the K vectors of the 2nd row of the initial matrix, and randomly inserts the J2 vectors into the K vectors of the 3rd row of the initial matrix; and so on, the control device randomly extracts J M vectors from the K vectors of the Mth row of the initial matrix, and randomly inserts the J M vectors into the K vectors of the 1st row of the initial matrix, thereby obtaining the physiological state matrix; wherein J 2…… = J M = J , J = Floor(K / 3), Floor() represents rounding down, the physiological state matrix is an M*(K+J) matrix, M is the number of rows of the physiological state matrix, and K+J is the number of columns of the physiological state matrix.

[0010] Optionally, the plurality of physiological state values included in each of the K physiological state parameters is X physiological state values, X is an integer greater than 2, the control device maps the plurality of physiological state values included in each of the K physiological state parameters into a vector, a total of K vectors, including: for any one of the K physiological state parameters: the control device maps the X physiological state values included in the physiological state parameter one by one into X initial vectors; the control device determines the vector with the maximum value in the X initial vectors as a reference vector; the control device determines X-1 relative vectors of the X-1 initial vectors with respect to the reference vector, a total of X-1 relative vectors, and the X-1 initial vectors are the vectors other than the reference vector in the X initial vectors; and the control device combines the X-1 relative vectors into a vector, that is, obtains a vector mapped by the physiological state parameter.

[0011] Optionally, the control device determines whether the monitored object enters the deep sleep state by processing the physiological state matrix through the neural network model, including: the control device inputs the physiological state matrix and a matrix obtained by conjugate transposition of the physiological state matrix into the neural network model for processing, to obtain a result output by the neural network model on whether the monitored object enters the deep sleep state.

[0012] Optionally, the control device adjusts the smart electric bed to a state suitable for the deep sleep state of the monitored object, including: the control device determines whether the smart electric bed needs to be adjusted according to the sleeping posture of the monitored object; if the smart electric bed needs to be adjusted, the control device gradually reduces the support degree of the area covered by the monitored object in the smart electric bed by a preset value; the control device collects M+N physiological state information, the M+N physiological state information being physiological state information of the monitored object in the M+N time period, N being an integer increasing from 1; and the control device determines whether to gradually increase the support degree of the area covered by the monitored object in the smart electric bed by a preset value according to the relationship between the M+N physiological state information and the physiological state information in the M time period in the M time period.

[0013] Optionally, the control device determines whether the smart electric bed needs to be adjusted according to the sleeping posture of the monitored object, including: the control device obtains the area of the force receiving area of the monitored object on the smart electric bed; if the area of the force receiving area of the monitored object is less than or equal to an area threshold, the control device determines that the sleeping posture of the monitored object is a high force sleeping posture; and the control device determines that the smart electric bed needs to be adjusted according to the sleeping posture of the monitored object being the high force sleeping posture.

[0014] Optionally, the control device determines whether to gradually increase the support degree of the area covered by the monitored object in the smart electric bed by a preset value according to the relationship between the M+N physiological state information and the physiological state information in the M time period in the M time period, including: the control device converts the M+N physiological state information into an M+N physiological state sequence, and converts the physiological state information in the M time period into an M physiological state sequence, the M+N physiological state sequence and the M physiological state sequence each containing K vectors; the control device determines a sequence correlation degree between the M+N physiological state sequence and the M physiological state sequence; if the sequence correlation degree is less than or equal to a correlation degree threshold, the control device determines to gradually increase the support degree of the area covered by the monitored object in the smart electric bed by a preset value, otherwise, no processing is performed.

[0015] In a second aspect, an intelligent electric bed adjustment system based on artificial intelligence is provided. The system includes a control device and an intelligent electric bed. The control device is configured to: collect physiological state information of a monitored object in each of M consecutive time periods, a total of M pieces of physiological state information, M being an integer greater than 2, the monitored object being located on the intelligent electric bed; process the M pieces of physiological state information through a neural network model to determine whether the monitored object enters a deep sleep state; and if the monitored object enters the deep sleep state, adjust the intelligent electric bed to a state suitable for the deep sleep state of the monitored object.

[0016] The system is specifically configured to perform the functions of the method of the first aspect described above, and is specifically understood with reference to the method of the first aspect described above, which will not be repeated here.

[0017] In a third aspect, a computer-readable storage medium is provided, including: a computer program or instructions; when the computer program or instructions are run on a computer, the computer program or instructions make the computer execute the method of the first aspect.

[0018] In a fourth aspect, a computer program product is provided, including a computer program or instructions, when the computer program or instructions are run on a computer, the computer program or instructions make the computer execute the method of the first aspect.

[0019] In summary, the above method and system have the following technical effects:

[0020] The control device can accurately determine whether the monitored object enters the deep sleep state by collecting physiological state information of the monitored object in each of M consecutive time periods, a total of M pieces of physiological state information, and analyzing the physiological state information through a neural network model. If the monitored object enters the deep sleep state, the control device adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object, thereby prolonging the duration of the deep sleep state. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 An architecture schematic diagram of the intelligent electric bed adjustment system based on artificial intelligence provided by the embodiments of the present application is provided.

[0022] Figure 2 A flowchart of the intelligent electric bed adjustment method based on artificial intelligence provided by the embodiments of the present application is provided.

[0023] Figure 3 A structure schematic diagram of the electronic device provided by the embodiments of the present application is provided. DETAILED DESCRIPTION

[0024] Various aspects, embodiments or features of the application can take the form of a system including a number of devices, components, modules, etc. It is understood that the various systems can include additional devices, components, modules, etc. and / or can not include all of the devices, components, modules, etc. discussed in connection with the figures. Additionally, a combination of these approaches can be used.

[0025] In embodiments of the present application, the indication can include direct indication and indirect indication, and can also include explicit indication and implicit indication. The information indicated by a certain information (such as the first indication information, the second indication information, or the third indication information, etc. below) is referred to as to-be-indicated information. In the implementation process, there are many ways to indicate the to-be-indicated information, for example, but not limited to, the to-be-indicated information can be directly indicated, such as the to-be-indicated information itself or the index of the to-be-indicated information, etc. The to-be-indicated information can also be indirectly indicated by indicating other information, where the other information and the to-be-indicated information have an association relationship. The to-be-indicated information can also be indicated only by a part of the to-be-indicated information, and the other part of the to-be-indicated information is known or agreed in advance. For example, the indication of a specific information can also be achieved by means of the arrangement order of various information agreed in advance (for example, specified by a protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common part of various information can be identified and uniformly indicated to reduce the indication overhead caused by separately indicating the same information.

[0026] In addition, the specific indication method can also be various existing indication methods, for example, but not limited to, the above-mentioned indication methods and various combinations thereof, etc. The specific details of various indication methods can refer to the prior art, which will not be described herein. As can be seen from the above, for example, when multiple information of the same type needs to be indicated, the indication methods of different information can not be the same. In the implementation process, the required indication method can be selected according to the specific needs, and the selected indication method is not limited by the embodiments of the present application. In this way, the indication method involved in the embodiments of the present application should be understood as covering various methods that can enable the to-be-indicated information to be known by the to-be-indicated party.

[0027] It should be understood that the to-be-indicated information can be sent as a whole, or can be sent separately into multiple sub-information, and the sending period and / or sending occasion of the sub-information can be the same or different. The specific sending method is not limited by the embodiments of the present application. The sending period and / or sending occasion of the sub-information can be pre-defined, for example, pre-defined according to a protocol, or can be configured by the sending end device by sending configuration information to the receiving end device.

[0028] The predefinition or preconfiguration can be realized by pre-storing corresponding codes, tables or other means for indicating relevant information in the device, and the embodiments of the present application do not limit the specific implementation manner. The storage can be in one or more memories, which can be separately arranged or integrated in the encoder or decoder, processor or communication device. The one or more memories can be partially separately arranged and partially integrated in the decoder, processor or communication device. The memory can be any form of storage medium, and the embodiments of the present application do not limit the same.

[0029] The protocol referred to in the embodiments of the present application can refer to a protocol family in the communication field, a standard protocol similar to the protocol family frame structure, or a relevant protocol applied to a future communication system, and the embodiments of the present application do not limit the same.

[0030] In the embodiments of the present application, the descriptions such as "when", "in the case of", "if" and the like refer to that the device will make corresponding processing under certain objective conditions, and are not limited in time, and do not require the device to have a judgment action when implemented, nor mean that there are other limitations.

[0031] In the description of the embodiments of the present application, unless otherwise specified, " / " represents that the objects before and after the correlation are in an "or" relationship, for example, A / B can represent A or B; "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. And, in the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. "At least one of the following" or the like means any combination of the items, including any combination of single item or multiple items. For example, at least one of a, b and c, or at least one of a, b or c, can represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, and c can be single or multiple. In addition, in order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, "first", "second", etc. are used to distinguish the same items or similar items with basically the same function and effect. The skilled in the art can understand that "first", "second", etc. do not limit the quantity and execution order, and "first", "second", etc. also do not limit that they must be different. At the same time, in the embodiments of the present application, "exemplary" or "for example" means to serve as an example, illustration or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, "exemplary" or "for example" is used to present the relevant concept in a specific manner, for understanding.

[0032] In order to facilitate understanding of the embodiments of the present application, first, taking the control system shown in FIG. 1 as an example, an exemplary control system is provided. Figure 1 The control system can include a control device and an intelligent electric bed. Figure 1 The control system can include a control device and an intelligent electric bed.

[0033] As shown in FIG. 1, the control system can include a control device and an intelligent electric bed. Figure 1

[0034] ​The control device can be a terminal device, i.e., a terminal. The terminal can be a terminal with a transceiver function, or a chip or chip system that can be provided in the terminal. The terminal can also be referred to as user equipment (UE), an access terminal, a subscriber unit, a subscriber station, a mobile station (MS), a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, a terminal, a wireless communication device, a user agent, or a user apparatus. The terminal in the embodiments of the present application can be a mobile phone, a cellular phone, a smart phone, a tablet computer (Pad), a wireless data card, a personal digital assistant (PDA), a wireless modem, a handset, a laptop computer, a machine type communication (MTC) terminal, a computer with a wireless transceiver function, a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical treatment, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, a vehicle-mounted terminal, a road side unit (RSU) with a terminal function, and the like. The terminal in the present application can also be a vehicle-mounted module, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip, or a vehicle-mounted unit built into a vehicle as one or more components or units. Alternatively, the terminal can also be customer-premises equipment (CPE).

[0035] An example, Figure 2 The flowchart of the intelligent electric bed adjustment method based on artificial intelligence provided by the embodiments of the present application is shown. The method can be applied to a control device.

[0036] As Figure 2 shown, the flow of the intelligent electric bed adjustment method based on artificial intelligence is as follows:

[0037] S201, the control device collects the physiological state information of the monitored object in each of the M consecutive time periods, a total of M physiological state information, M is an integer greater than 2, the monitored object (i.e. the user who is sleeping) is located on the smart electric bed, and the monitored object wears the body sign detection device attached to the smart electric bed, which can collect the physiological state information of the user. For the i-th physiological state information in the M physiological state information, i is any integer from 1 to M, the i-th physiological state information includes physiological state parameters collected at K time points in the i-th time period in the M time periods, a total of K physiological state parameters, K is an integer greater than 2, and any one of the K physiological state parameters includes a plurality of physiological state values, such as heart rate, respiratory rate, body temperature, blood pressure, etc. For example, each time period is 30 seconds, K = 15, which means that a physiological state parameter is collected every 2 seconds in each time period, and each time period corresponds to a physiological state information containing 15 physiological state parameters. The control device can collect physiological state information in the form of a sliding window, for example, M = 4, at the end of the fourth time period, the control device obtains the physiological state information in the first to fourth time periods, then at the end of the fifth time period, the control device obtains the physiological state information in the second to fifth time periods, then at the end of the sixth time period, the control device obtains the physiological state information in the third to sixth time periods, and so on.

[0038] S202, the control device processes the M physiological state information through a neural network model to determine whether the monitored object enters a deep sleep state.

[0039] S202 can include the following steps:

[0040] Step 1: The control device can convert the M physiological state information into a physiological state matrix.

[0041] For example, the control device can convert each of the M pieces of physiological state information into a set of vectors, obtaining M sets of vectors, each set of vectors in the M sets of vectors containing parameters corresponding to a row in the initial matrix. Wherein, the control device can map each physiological state parameter in the K physiological state parameters to a vector, obtaining K vectors, the K vectors being the i-th set of vectors in the M sets of vectors, obtaining the M sets of vectors when i traverses from 1 to M, the initial matrix being a matrix of M*K, M being the number of rows of the initial matrix, and K being the number of columns of the initial matrix. Specifically, the X physiological state values included in each physiological state parameter in the K physiological state parameters are X physiological state values, X being an integer greater than 2, and for any physiological state parameter in the K physiological state parameters, the control device can map the X physiological state values included in the physiological state parameter one-to-one to X initial vectors. In one example, assuming that X=4, including 4 physiological state values of heart rate, respiratory rate, body temperature, and blood pressure, the 4 parameters in a physiological state parameter can be one-to-one mapped to 4 initial vectors, such as vector 1, vector 2, vector 3, and vector 4. The control device determines the vector with the maximum value in the X initial vectors as a reference vector; the control device determines X-1 relative vectors of each of the X-1 initial vectors relative to the reference vector, obtaining X-1 relative vectors, and the X-1 initial vectors being vectors other than the reference vector in the X initial vectors; and the control device combines the X-1 relative vectors into one vector, i.e., obtains a vector mapped by the physiological state parameter. Continuing the above example, assuming that vector 1 is the vector with the maximum value, the control device can determine the end point of vector 1 to the end point of vector 2 as relative vector 1, the end point of vector 1 to the end point of vector 3 as relative vector 2, and the end point of vector 1 to the end point of vector 4 as relative vector 3, and then the control device can combine relative vector 1, relative vector 2, and relative vector 3 into one vector, such as multiplying or adding the three relative vectors, thereby obtaining a vector mapped by a physiological state parameter. It can be understood that since the mapping of the vectors determines the relative vectors between the parameters, the vector thus obtained can enable the correlation between the X physiological state values in a physiological state parameter, thereby enabling the model to better analyze whether it enters a deep sleep state.

[0042] Subsequently, the control device performs a row association operation on the initial matrix to obtain a physiological state matrix. For example, the control device randomly extracts J1 vectors from the K vectors in the 1st row of the initial matrix and randomly inserts the J1 vectors into the K vectors in the 2nd row of the initial matrix; the control device randomly extracts J2 vectors from the K vectors in the 2nd row of the initial matrix and randomly inserts the J2 vectors into the K vectors in the 3rd row of the initial matrix; and so on, the control device randomly extracts J Ma vector, and J M a vector is randomly inserted into the K vectors of the first row of the initial matrix, and thus, the physiological state matrix is obtained; wherein, J1=J 2…… =J M =J, J=Floor(K / 3), Floor() represents rounding down, the physiological state matrix is a matrix of M*(K+J), M is the number of rows of the physiological state matrix, and K+J is the number of columns of the physiological state matrix. In this way, not only can the matrix be expanded for estimation, thereby increasing the amount of data input into the model analysis and improving the robustness of the model, but also because each row contains the vector of the previous row, the coupling between parameters of different time periods can be increased, avoiding the isolation of parameters that can cause the model to not be able to well utilize the trend of parameter changes in the processing process for analysis, which can also improve the robustness of the model, thereby more accurately determining whether the monitored object enters a deep sleep state.

[0043] Step 2: The control device can process the physiological state matrix through the neural network model to determine whether the monitored object enters a deep sleep state.

[0044] The control device can input the physiological state matrix and the matrix after the physiological state matrix is conjugate transposed into the neural network model for processing to obtain the result of whether the monitored object enters a deep sleep state output by the neural network model, so as to further improve the accuracy of the analysis result.

[0045] S203, if the monitored object enters a deep sleep state, the control device adjusts the smart electric bed to a state suitable for the deep sleep state of the monitored object.

[0046] The control device determines whether the smart electric bed needs to be adjusted according to the sleep posture of the monitored object. For example, the control device obtains the area of the force receiving area of the monitored object on the smart electric bed. Wherein, the bed surface of the smart electric bed is gridded, and each grid is provided with a corresponding pressure sensor, so that the control device can determine the area of the force receiving area of the monitored object according to the number of grids where the pressure sensor with feedback pressure is located.

[0047] If the area of the force-receiving region of the monitored object is less than or equal to the area threshold, the control device determines that the sleeping posture of the monitored object is a high-force sleeping posture, such as side sleeping, near side sleeping, or inclined sleeping, and the like, in which the body pressure is not as dispersed as possible on the mattress. Of course, if the sleeping posture of the monitored object is a non-high-force sleeping posture, such as lying sleeping, the control device can not perform control processing. Therefore, the control device can determine that the smart electric bed needs to be adjusted according to the sleeping posture of the monitored object being a high-force sleeping posture. Further, if the smart electric bed needs to be adjusted, the control device (such as controlling the smart electric bed to electrically adjust) gradually reduces the support degree of the region covered by the monitored object in the smart electric bed by a preset value (such as reducing the current support degree by 10-15%, i.e., reducing the hardness of the mattress), wherein the region covered by the monitored object can be drawn based on the position of the grid where the pressure sensor that feeds back the pressure is located, such as drawing the region covering these grids, i.e., the region covered by the monitored object. It can be understood that the granularity of the grid needs to be as fine as possible, such as 100*180. In this way, the pressure generated by the sleeping posture of the monitored object can be better shared by the mattress, i.e., the area of the force-receiving region is increased, so that the monitored object is not easily caused to change the sleeping posture due to limb force compression, thereby ending the deep sleep state. Subsequently, the control device can collect the M+Nth physiological state information, the M+Nth physiological state information being the physiological state information of the monitored object in the M+Nth time period, N being an integer that increases from 1; the control device determines whether to gradually increase the support degree of the region covered by the monitored object in the smart electric bed by a preset value according to the relationship between the M+Nth physiological state information and the physiological state information in the Mth time period in the M time periods. For example, the control device can convert the M+Nth physiological state information into an M+Nth physiological state sequence (which can be understood as a row in the above matrix, containing K vectors), and convert the physiological state information in the Mth time period into an Mth physiological state sequence (which can also be understood as a row in the above matrix, containing K vectors), i.e., the M+Nth physiological state sequence and the Mth physiological state sequence each contain K vectors; the control device can determine the sequence correlation degree between the M+Nth physiological state sequence and the Mth physiological state sequence, which can specifically be the inner product of the sequences; if the sequence correlation degree is less than or equal to the correlation degree threshold, the control device determines to gradually increase the support degree of the region covered by the monitored object in the smart electric bed by a preset value, otherwise, no processing is performed. That is, if the sequence correlation degree is less than or equal to the correlation degree threshold, it means that the physiological state of the monitored object has changed, which can end the deep sleep state, and therefore the hardness of the mattress can be restored at this time to avoid the monitored object ending the deep sleep state due to being in a soft mattress state for a long time, and in this way the deep sleep state can be prolonged as much as possible.

[0048] In conclusion, the control device can accurately determine whether the monitored object enters the deep sleep state by collecting the physiological state information of the monitored object in the continuous M time periods, i.e., M physiological state information, and analyzing the physiological state information through the neural network model; if the monitored object enters the deep sleep state, the control device adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object, thereby prolonging the time length of the deep sleep state.

[0049] Figure 3 The structure schematic diagram of the electronic device provided by the embodiment of the present application is shown. The electronic device can be a terminal device, or a chip (system) or other components or assemblies which can be arranged in the terminal device. As shown in the figure, the electronic device 400 can include a processor 401. Optionally, the electronic device 400 can also include a memory 402 and / or a transceiver 403. The processor 401 is coupled with the memory 402 and the transceiver 403, for example, through a communication bus. In addition, the electronic device 400 can also be a chip, for example, including the processor 401, at this time, the transceiver can be an input / output interface of the chip. Figure 3

[0050] The specific introduction of the various constituent components of the electronic device 400 will be given below. Figure 3 The specific introduction of the various constituent components of the electronic device 400 will be given below.

[0051] The processor 401 is the control center of the electronic device 400, and can be one processor or a plurality of processing elements. For example, the processor 401 is one or more central processing units (CPU), application specific integrated circuits (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application, for example, one or more microprocessors (digital signal processor, DSP), or one or more field programmable gate arrays (FPGA).

[0052] Optionally, the processor 401 can execute various functions of the electronic device 400 by running or executing software programs stored in the memory 402 and calling data stored in the memory 402, for example, executing the method shown in the above. Figure 2

[0053] In a specific implementation, as an embodiment, the processor 401 can include one or more CPUs, for example, the CPU0 and the CPU1 shown in the figure. Figure 3

[0054] ​​​In a particular implementation, the electronic device 400 can also include multiple processors as an embodiment. Each of the processors can be a single-CPU or a multi-CPU. The processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer programs or instructions).

[0055] The memory 402 is configured to store software programs for implementing the solutions of the present application, and the processor 401 is configured to control the execution of the software programs. The implementation manners can refer to the above-mentioned method embodiments, and will not be described here.

[0056] Optionally, the memory 402 can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magneto-optical disk storage (including a compact disk, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program codes in the form of instructions or data structures and can be accessed by a computer, but not limited to the above. The memory 402 can be integrated with the processor 401 or exist independently, and is coupled with the processor 401 through an interface (not shown) of the electronic device 400, and the embodiments of the present application are not limited in this regard. Figure 3

[0057] The transceiver 403 is configured to communicate with other electronic devices. For example, the electronic device 400 is a terminal device, and the transceiver 403 can be configured to communicate with a network device or another terminal device. For another example, the electronic device 400 is a network device, and the transceiver 403 can be configured to communicate with a terminal device or another network device.

[0058] Optionally, the transceiver 403 can include a receiver and a transmitter (not shown separately in the figure). The receiver is configured to implement the receiving function, and the transmitter is configured to implement the transmitting function. Figure 3

[0059] ​​Optionally, the transceiver 403 can be integrated with the processor 401, or exist independently, and is coupled with the processor 401 through an interface circuit (not shown in the figure) of the electronic device 400, and the embodiments of the present application do not make a specific limitation hereon. Figure 3 The transceiver 403 can be integrated with the processor 401, or exist independently, and is coupled with the processor 401 through an interface circuit (not shown in the figure) of the electronic device 400, and the embodiments of the present application do not make a specific limitation hereon.

[0060] It can be understood that, Figure 3 The structure of the electronic device 400 shown in the figure does not constitute a limitation on the electronic device, and an actual electronic device can include more or less components than those shown in the figure, or combine certain components, or different component arrangements.

[0061] In addition, the technical effects of the electronic device 400 can refer to the technical effects of the methods described in the above method embodiments, which will not be described here again.

[0062] It should be understood that the processor in the embodiments of the present application can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), ready programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0063] It should also be understood that the memory in the embodiments of the present application can be volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Where the nonvolatile memory is, for example, read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically EPROM (EEPROM), or flash memory. The volatile memory, which can be used as external cache, can be, for example, random access memory (RAM). By way of example, and not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0064] The above-described embodiments can be implemented in whole or in part by software, hardware (e.g., circuitry), firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer programs or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer programs or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired (e.g., infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0065] It should be understood that the term "and / or" herein merely describes an association relationship of associated objects, which means that there can be three relationships, for example, A and / or B can represent three cases of A alone, A and B together, and B alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects, but can also represent an "and / or" relationship, which can be understood in the context before and after.

[0066] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of the items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0067] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-described processes does not mean the order of execution, and the execution order of the processes should be determined by their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0068] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0069] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0070] In several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented by other ways. For example, the above-described device embodiments are merely schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0071] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0072] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0073] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0074] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An artificial intelligence-based intelligent electric bed adjustment method, characterized by, The method is applied to a control device, and comprises the following steps: The control device collects physiological state information of a monitored object in M continuous time periods, and obtains M pieces of physiological state information, where M is an integer greater than 2, and the monitored object is located on an intelligent electric bed; The control device converts each piece of the M pieces of physiological state information into a vector set, and obtains M vector sets, each vector set in the M vector sets contains parameters in a corresponding row of an initial matrix; The control device performs a row association operation on the initial matrix to obtain a physiological state matrix; The control device processes the physiological state matrix through a neural network model to determine whether the monitored object enters a deep sleep state; If the monitored object enters the deep sleep state, the control device adjusts the intelligent electric bed to a state suitable for the deep sleep state of the monitored object; For the i-th piece of physiological state information in the M pieces of physiological state information, i is any integer from 1 to M, the i-th piece of physiological state information includes physiological state parameters collected at K time points in the i-th time period in the M time periods, and there are K physiological state parameters, where K is an integer greater than 2, and any physiological state parameter in the K physiological state parameters includes multiple physiological state values; accordingly, the control device converts each piece of the M pieces of physiological state information into a vector set, and obtains M vector sets, which include the following steps: The control device maps the multiple physiological state values included in each physiological state parameter in the K physiological state parameters into a vector, and obtains K vectors, the K vectors are the i-th vector set in the M vector sets, the M vector sets are obtained when i traverses from 1 to M, the initial matrix is a matrix of M*K, M is the number of rows of the initial matrix, and K is the number of columns of the initial matrix; Correspondingly, the control device performs a row association operation on the initial matrix to obtain the physiological state matrix, which includes the following steps: The control device randomly extracts J1 vectors from the K vectors in the first row of the initial matrix, and randomly inserts the J1 vectors into the K vectors in the second row of the initial matrix; The control device randomly extracts J2 vectors from the K vectors in the second row of the initial matrix, and randomly inserts the J2 vectors into the K vectors in the third row of the initial matrix; By analogy, the control device randomly extracts J M vectors from the K vectors of the Mth row of the initial matrix, and randomly inserts the J M vectors into the K vectors of the 1st row of the initial matrix, thus obtaining the physiological state matrix. wherein J1=J 2…… =J M =J, J=Floor(K / 3), Floor() denotes rounding down, the physiological state matrix is a matrix of M*(K+J), M is the number of rows of the physiological state matrix, and K+J is the number of columns of the physiological state matrix.

2. The method of claim 1, wherein, The multiple physiological state values included in each physiological state parameter in the K physiological state parameters are X physiological state values, where X is an integer greater than 2, the control device maps the multiple physiological state values included in each physiological state parameter in the K physiological state parameters into a vector, and obtains K vectors, which include the following steps: For any physiological state parameter in the K physiological state parameters: The control device maps the X physiological state values included in the physiological state parameter into X initial vectors one by one; The control device determines a vector with the maximum value in the X initial vectors as a reference vector; The control device determines X-1 initial vectors each relative to the reference vector, a total of X-1 relative vectors, the X-1 initial vectors being vectors other than the reference vector among the X initial vectors; The control device synthesizes the X-1 relative vectors into one vector, that is, a vector mapped by the physiological state parameter.

3. The method of claim 1, wherein, The control device processes the physiological state matrix through the neural network model to determine whether the monitored object enters a deep sleep state, including: The control device inputs the physiological state matrix and the matrix after the physiological state matrix is conjugate transposed into the neural network model for processing to obtain a result output by the neural network model that the monitored object enters a deep sleep state.

4. The method of claim 1, wherein, The control device adjusts the smart electric bed to a state suitable for the deep sleep state of the monitored object, including: The control device determines whether the smart electric bed needs to be adjusted according to the sleeping posture of the monitored object; If the smart electric bed needs to be adjusted, the control device gradually reduces the support degree of the area covered by the monitored object in the smart electric bed by a preset value; The control device collects M+N physiological state information, the M+N physiological state information being physiological state information of the monitored object in an M+N time period, N being an integer that increases from 1; The control device determines whether to gradually increase the support degree of the area covered by the monitored object in the smart electric bed by the preset value according to the relationship between the M+N physiological state information and the physiological state information in the M time period.

5. The method of claim 4, wherein, The control device determines whether the smart electric bed needs to be adjusted according to the sleeping posture of the monitored object, including: The control device obtains the area of the force receiving area of the monitored object on the smart electric bed; If the area of the force receiving area of the monitored object is less than or equal to an area threshold, the control device determines that the sleeping posture of the monitored object is a high-force sleeping posture; The control device determines that the smart electric bed needs to be adjusted according to the sleeping posture of the monitored object being a high-force sleeping posture.

6. The method of claim 5, wherein, The control device determines whether to gradually increase the support degree of the area covered by the monitored object in the smart electric bed by the preset value according to the relationship between the M+N physiological state information and the physiological state information in the M time period, including: The control device converts the M+N physiological state information into an M+N physiological state sequence and converts the physiological state information in the M time period into an M physiological state sequence, the M+N physiological state sequence and the M physiological state sequence each containing K vectors; The control device determines a sequence correlation degree between the M+N physiological state sequence and the M physiological state sequence; The control device determines a sequence correlation degree between the M+N physiological state sequence and the M physiological state sequence; If the sequence correlation degree is less than or equal to a correlation degree threshold, the control device determines to gradually increase the support degree of the area covered by the monitored object in the smart electric bed to the preset value, otherwise, no processing is performed.

7. An artificial intelligence-based smart electric bed adjustment system, characterized by, The system comprises a control device and a smart electric bed, and the control device is configured to: The control device collects physiological state information of a monitored object in each of M consecutive time periods, a total of M pieces of physiological state information, M being an integer greater than 2, and the monitored object being located on a smart electric bed; The control device converts each of the M pieces of physiological state information into a vector set, a total of M vector sets, each vector set in the M vector sets containing parameters corresponding to a row in the initial matrix; The control device performs a row association operation on the initial matrix to obtain a physiological state matrix; The control device processes the physiological state matrix through a neural network model to determine whether the monitored object enters a deep sleep state; If the monitored object enters a deep sleep state, the control device adjusts the smart electric bed to a state suitable for the deep sleep state of the monitored object; For the i-th physiological state information in the M pieces of physiological state information, i being any integer from 1 to M, the i-th physiological state information includes physiological state parameters collected at K time points in the i-th time period in the M time periods, a total of K physiological state parameters, K being an integer greater than 2, and any physiological state parameter in the K physiological state parameters including multiple physiological state values; correspondingly, the control device converts each of the M pieces of physiological state information into a vector set, a total of M vector sets, including: The control device maps the multiple physiological state values included in each physiological state parameter in the K physiological state parameters into a vector, a total of K vectors, the K vectors being the i-th vector set in the M vector sets, the M vector sets being obtained when i iterates from 1 to M, the initial matrix being an M*K matrix, M being the number of rows of the initial matrix, and K being the number of columns of the initial matrix; Correspondingly, the control device performs a row association operation on the initial matrix to obtain the physiological state matrix, including: The control device randomly extracts J1 vectors from the K vectors of the 1st row of the initial matrix and randomly inserts the J1 vectors into the K vectors of the 2nd row of the initial matrix; The control device randomly extracts J2 vectors from the K vectors of the 2nd row of the initial matrix and randomly inserts the J2 vectors into the K vectors of the 3rd row of the initial matrix; By analogy, the control device randomly extracts J M vectors from the K vectors of the Mth row of the initial matrix, and randomly inserts the J M vectors into the K vectors of the 1st row of the initial matrix, thus obtaining the physiological state matrix. wherein J1=J 2…… =J M =J, J=Floor(K / 3), Floor() denotes rounding down, the physiological state matrix is a matrix of M*(K+J), M is the number of rows of the physiological state matrix, and K+J is the number of columns of the physiological state matrix.

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